Articles published in Volume 11

Volume 11

An efficient Feed-forward compensation mechanism for NFC-based PMSM system using different observers for Load-torque estimation

The Permanent magnet synchronous motor (PMSM) offers high torque and efficiency and is used in most industrial applications. This manuscript uses a feed-forward compensation mechanism to design an efficient neuro-fuzzy logic controller (NFC) based surface-mounted PMSM system. The different load-observers like Discrete Luenberger Observer (DLO), Kalman filter observer (KFO), and discrete Kalman filter observer (DKFO) are used as a feed-forward compensation method to compensate the dq stator current and also estimate performance metrics. The NFC is used as a speed controller, and two PI controllers are used for the current control mechanism. The noise is added at the actual load torque and speed of PMSM and compensated using load observers.

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System Modelling and Identification for EEG Monitoring using Random Vector Functional Link Network

Brain signal research occupies a special position in recent biomedical research in recent times. In this work, the authors try to develop a model for monitoring the EEG signal of the patient. It is the extrinsic application of the system identification problem. The Random Vector functional link network (RVFLN) model as the variant of Neural Network, is proposed for the dynamic modeling of a practical system. RVFLN is a fast-learning feed-forward network and does not need iterative tuning that reduces the model's computational complexity and faster training performance.

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Improved Hybrid Routing Protocol (IHRP) in MANETs Based on Situation Based Adaptive Routing

Without the need of a fixed foundation or base station, the Mobile Ad hoc Network creates its own wireless network. One of the most troublesome aspects of Mobile Ad hoc Network (MANET) is the occurrence of unexpected loss of network connectivity. As a result of this problem, packets continue to drop, and we must restore the connection by sending Route Request (RREQ) and Route Reply (RREP). As a result, network performance will suffer yet another setback. We used the scenario routing technique to combine the Dream Multipath Routing (DMR), Ad hoc on-demand multipath distance vector (AOMDV), Optimized link-state routing (OLSR), and Ad-hoc on Demand Vector (AODV) routing protocols to build the IHRP routing protocol in this work. According to previous studies, (AODV) is more suited when node motion is high.

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A Systematic Approach of Advanced Dilated Convolution Network for Speaker Identification

Over the years, the Speaker recognition area is facing various challenges in identifying the speakers accurately. Remarkable changes came into existence with the advent of deep learning algorithms. Deep learning made a remarkable impact on the speaker recognition approaches. This paper introduces a simple novel architectural approach to an advanced Dilated Convolution network. The novel idea is to induce the well-structured log-Melspectrum to the proposed dilated convolution neural network and reduce the number of layers to 11. The network utilizes the Global average pooling to accumulate the outputs from all layers to get the feature vector representation for classification.

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Optimal Placement of PMUs in Smart Grid for Voltage Stability Monitoring using AMPSO and PSAT

Efficient energy use is critical for a growing nation like India. The smart grid (SG) idea enables the creation of a highly dependable electricity system that optimizes existing resources. The Indian electricity grid as it now exists needs fundamental modifications to satisfy increasing demand and to make the system more intelligent and dependable. Since the past several decades, power system stability has been seen as a significant challenge to power system researchers and utilities. With a not many strategically placed Phasor Measurement Units (PMUs), it may be feasible to observe the power system stability of the network. This article suggests an optimum location for PMUs, considering the effect of power system stability-related serious situations.

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ISM Band 2.4 GHz Wearable Textile Antenna for Glucose Level Monitoring

Wearable technology has recently attracted much interest for various uses. An essential component of the wearable system is the wearable antenna. Textile and non-textile materials have both been used to create wearable antennas. Textile antennas are very useful and widely used nowadays, particularly in body-worn applications monitoring health parameters. Fabricated using microstrip technology, textile antennas have various benefits, including small size, lightweight, simple fabrication, and ease of wear. In this study, a microstrip antenna is created utilizing a substrate made of jeans. It works between 2.4 to 2.5 GHz in the ISM (industrial, scientific, and medical) band. High-frequency structure simulator (HFSS) software was used to simulate two antennas, one with an incomplete and the other with a complete ground plane. Wearable antennas can protect the body from the impacts of RF radiation by utilizing the entire ground plane principle.

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A Novel Congestion Control Scheme using Firefly Algorithm Optimized Fuzzy-PID Controller in Wireless Sensor Network

Wireless Sensor Networks (WSNs) consist of several sensor nodes, each of which may collect, receive and transmit data. In recent years, WSNs have emerged as essential technologies due to their ubiquity in applications such as the military, smartphones, disaster management, healthcare monitoring, and other surveillance systems. The inability to send data from the sensor node promptly and the impossibility of new data reaching the node's queue indicate of network congestion. The packet will be either discarded or delayed, which will cause more data loss, longer transmission delays, reduced network throughput, and lower network quality of service. To address this problem, this paper proposes an efficient and novel Firefly Algorithm-optimized Fuzzy-PID (FA-Fuzzy-PID) controller for congestion control in Wireless Sensor Networks (WSNs). The proposed control technique used a fuzzy control algorithm to overcome the standard PID controller's slow optimization parameter, low calculation accuracy, and limited adaptability.

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Impact of Stator Slot Shape on Cogging Torque of BLDC Motor

Brushless DC (BLDC) motors have a wide range of applications in these modern days, such as electric vehicles, industrial robots, washing machines, pumps, and blowers. The brushless DC motors have many advantages when compared to induction motors and conventional DC motors, such as better speed control, noiseless operation, high efficiency, less maintenance, and a long life. Along with these benefits, there is one major disadvantage known as cogging, which causes undesirable effects in the motor such as noises and vibrations. BLDC motors have been widely used in automation and industrial applications due to their attractive features. There are certain parameters to be considered while designing a BLDC motor, such as its dimensions, number of windings turns, type of magnetic materials used, required torque, output current, slot-to-depth ratio, efficiency, temperature rise, etc.

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Enhancement of Voltage Regulation and Transient Stability of Six Phase STATCOM using Decoupled Current Control Strategy

In this paper, STATCOM is used in a six-phase transmission system to improve voltage regulation and transient stability. Six-phase pulse width modulated voltage source converters are used in the design of STATCOM, and a decoupled current control approach is used to operate it. The voltage references used by the voltage source converters' pulse generator are produced using a decoupled control method. This control technique allows for the decoupling of the coupling effect or the dependence of the d and q currents on one another, which improves system performance under unusual and abnormal conditions. By using the pulse generator of voltage source converters, the inner current control loop and the outer voltage control loop are intended to produce the necessary reference voltages. Simulation results are presented using MATLAB/SIMULINK to check efficacy of proposed six phase STATCOM.

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Context-Aware Offloading for IoT Application using Fog-Cloud Computing

It is difficult to run delay-sensitive applications and the cloud simultaneously due to performance metrics such as latency, energy consumption, bandwidth, and response time exceeding threshold levels. This is the case even though advanced networks and technologies are being used. The middleware layer of the Internet of Things (IoT) architecture appears to be a promising solution that could be used to deal with these issues while still meeting the need for high task offloading criterion. The research that is being proposed recommends implementing Fog Computing (FC) as smart gateway in middleware so that it can provide services the edge of the networks. Applications that are sensitive to delays would then be able to be provided in an efficient manner as a result of this.

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Design and Characterization of a Novel FinFET based NCL Cell Library for High Performance Asynchronous Circuits

In recent times, synchronous circuits are facing design related issues like clock skew, glitch power, EMI, leakage power, etc. The clock-less design paradigm – Asynchronous design challenges most of these issues and accepted as a better alternative to clocked circuits. QDI based Null Convention Logic (NCL) is such a clock-less design concept. However, NCL designs couldn’t get wide spread acceptance due to unavailability of commercial CAD tools and design compatible NCL standard cell library. The proposed research work in this paper demonstrates design and characterization of FinFET based NCL cell library to facilitate QDI based asynchronous circuit design

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Modelling and Simulation of MPPT Based Solar Photovoltaic System for Voltage Lift LUO Converter

Several maximum PowerPoint tracking techniques have been proposed to achieve optimal matching between solar photovoltaic arrays to load for extracting maximum available power from photovoltaic source. These techniques differ in terms of tracking speed, complexity, cost, accuracy of tracking, number of sensors used etc., This article proposes an Adaptive Neuro Fuzzy-Inference System (ANFIS) based maximum PowerPoint tracking for positive-output voltage lift LUO converter based on solar (PV)system for resistant load application. Performance analysis of proposed (MPPT) technique has been evaluated in MATLAB/Simulink environment for four different cases of input radiation and temperature patterns

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ANN-SOGI-based Shunt Active Power Filter for Harmonic Mitigation

In this paper introduces a PV based generation system interlinked with shunt active power filter (SAPF) to provide the effective reactive power compensation and mitigation of harmonics. The SAPF is comprised of a photovoltaic generation system, DC link capacitor and voltage source inverter (VSI). The current harmonics caused by nonlinear loads can be greatly reduced with the help of active power filter. To generate the reference current and the regulation of SAPF, the artificial neural network is proposed. The Second Order Generalized Integrator (SOGI) with Artificial Neural Network (ANN) controller is engaged to calculate the reference source current for SAPF. ANN additionally boasts great compatibility for digital implementation, control performance, and lightning-fast dynamic reaction.

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Evaluation of IIOT based Pd-MaaS using CNN with Ensemble Subspace Discriminate – for Indian Ship Building in Maritime Industry

Indian shipbuilding has a long history in the maritime industry dating back to the origin of civilization. India's shipbuilding sector is primarily concentrated in its coastal regions. Due to capacity constraints and decreased shipbuilding prices in emerging nations, shipbuilding activities has changed. This has created fresh opportunities for the Indian shipbuilding industry. The prospects for the Indian shipbuilding sector are improved by rising global trade and strong need for modern boats. This study investigates the use of Predictive Maintenance as a Service on the Industrial Internet of Things (IIoT-PdMaaS). Artificial intelligence (AI) in the maritime industry has numerous major benefits, including improved decision-making analysis, automation, security, route planning, and increased efficiency.

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Array Design Using Genetic Algorithm for the Generation of Sum and Difference Patterns

In the field of pattern synthesis, the design of arrays to synthesis optimized sum and difference patterns in a successive manner is an important problem in most of the radars. Although several researchers published many papers in the open literature, investigations are made by the authors to design arrays of discrete radiators using a well-formulated Genetic Algorithm (GA). The practical constraints are taken into account to design an amplitude distribution to produce the sum pattern with desired side lobe levels without broadening the main beam. Such sum patterns are found to be very useful in high-resolution radars where the EMI problems are also addressed.

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Fuzzy and PSO tuned PI controller based SAPF for Harmonic Mitigations

The development of a reliable power filter is essential for meeting the need for high-quality power. Current and voltage harmonics are a major contributor to poor power quality and must be eliminated. Shunt active power filters (SAPFs) can be installed to reduce the negative effects of harmonics. Fuzzy logic and proportional integral (PI) controllers excel at regulating DC link voltage in shunt active power filters (SAPFs). This research assessed how well Particle Swarm Optimization controls the DC link voltage in a Shunt Active Power Filter (SAPF), thereby mitigating harmonics. The appropriate PI control parameters are first determined using the Particle Swarm Optimization technique. The PSO-tuned PI parameters are combined with a fuzzy logic controller for the SAPF simulation.

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A Performance Analysis of Massive MIMO System using Antenna Selection Algorithms

A large number of transmitting components makes Massive Multiple-Input Multiple-Output (MIMO) one of the most hopeful solution for the 5G technology. However, a large antenna system boosts the hardware intricacy and cost of the system because of RF transceivers used at the base station for every antenna element. Hence, antenna selection is one of the most effective schemes to select a good subset of antennas with the finest channel circumstances and contribute maximum to the channel capacity. This paper presents Branch and Bound (BAB) algorithm for efficient antenna selection in Massive MIMO technology.

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Simulation of Solar Based Smart Grid System Using Artificial Neural Network and Fuzzy Controller

To promote the economy and reliability of the energy trading systems, the use of interconnected smart grids is encouraging. A distributed energy management plan for the interconnected operation of the smart grid that maximizes the resident intake of renewable energy is required during operation. On the client side, possibilities and actions are being discussed in the research papers to incorporate the renewable energy sources. In this paper, the use of Artificial Intelligent Techniques to manage energy or power supply to meet the electricity demand of customers is illustrated. Simulation has been done using wind and solar power supply to manage the load demand for the client side in a smart grid system.

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Inductance Estimation of PMSM Using Extended Kalman Filter

Estimation of parameters of Permanent Magnet Synchronous Motor (PMSM) plays an important role for motor controller tuning in Electric Vehicle (EV) application. Under running condition motor parameters vary due to different effects such as temperature, saturation and Voltage Source Inverter (VSI) non-linearities. Identification of parameters in running condition increases the control performance of system. This paper uses Extended Kalman Filter (EKF), which allows estimation of d-q axis inductances of PMSM. The control algorithm considered here is Field Oriented Control (FOC) for EV system having position sensor. The simulation is performed using MATLAB- Simulink software.

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Staying Ahead of Threats: A Review of AI and Cyber Security in Power Generation and Distribution

The integration of artificial intelligence (AI) and the Internet of Things (IoT) in the power generation and distribution industry presents opportunities and challenges, particularly in the area of cybersecurity. Previous studies have explored the potential of AI to enhance cybersecurity in power systems, but limitations in terms of sample size and scope have hindered a comprehensive understanding of the current state of the field. To address this gap, this paper presents a systematic literature review of 30 papers that analyzes and categorizes relevant research based on their focus on threats, solutions, and future trends. The results indicate that 30 articles provide evidence supporting the use of AI and machine learning techniques to significantly enhance cybersecurity in the power sector.

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Power Quality Analysis of Fuzzy DVR based Hybrid Solar PV-PEMFC System Under Severe Disturbance

Hybrid Energy System (HES) is becoming popular as it is identified as the most safe, sustainable, and long-term option for energy management. The grid-connected power network is vulnerable to frequent interruptions, which can lead to instability and a huge blackout. Furthermore, the presence of switching devices in industrial and domestic applications causes disturbances such as voltage swell/sag, waveform distortion, interruptions, impulsive voltage, and so on. Dynamic voltage restorer, known as a modern power compensating device, is cost effective, compact in size and can handle more energy capacity than DTSATCOM and UPFC.

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Power Quality Analysis of Fuzzy DVR based Hybrid Solar PV-PEMFC System Under Severe Disturbance

Hybrid Energy System (HES) is becoming popular as it is identified as the most safe, sustainable, and long-term option for energy management. The grid-connected power network is vulnerable to frequent interruptions, which can lead to instability and a huge blackout. Furthermore, the presence of switching devices in industrial and domestic applications causes disturbances such as voltage swell/sag, waveform distortion, interruptions, impulsive voltage, and so on. Dynamic voltage restorer, known as a modern power compensating device, is cost effective, compact in size and can handle more energy capacity than DTSATCOM and UPFC.

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A Dual Notch Band UWB Antenna for Local Area Cognitive Radio Network Applications

Over the years, Cognitive radio technology has dramatically influenced wireless systems in which antenna plays a significant role for interviewing spectrum sensing and underlay operations. An ultra-wideband (UWB) antenna with extended dual notch band feature is proposed in this work to meet the requirements of cognitive radios without interference from the crowded spectrum. The proposed antenna consists of stair-stepped rectangular patch with circular cut off the patch at the lower edges to which a complementary split ring resonator (CSRR) and a Patch Stub are loaded to achieve notch bands

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Effect of Test Cable Termination on Frequency Response of Transformer Winding

Sweep Frequency Response Analysis (SFRA) method is the most powerful tool to predict the condition of transformer winding. The reliable measurement of frequency response is equally important as its interpretation. A few standards (IEEE std. C57.149-2012, IEC 60076, the Chinese Electrical Power Industry Standard ICS27.100.F24-2005) & much research work have been published, stating dos & don’ts while measuring the frequency response of transformer winding. In this paper, an attempt is made to introduce an additional factor affecting frequency response, while doing measurements

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A Novel Spider Monkey Optimized Fuzzy C-Means Algorithm (SMOFCM) for Energy-Based Cluster-Head Selection in WSNs

AI is getting increasingly complex as a result of its widespread deployment, making energy efficiency in Wireless Sensor Network (WSN)-based Internet of Things (IoT) systems a highly difficult problem to solve. In energy-constrained networks, cluster-based hierarchical routing protocols are a very efficient technique for transferring data between nodes. In this paper, a novel Spider Monkey Optimized Fuzzy C-Means Algorithm (SMOFCM) is proposed to improve the lifetime of the network and less energy consumption. The proposed SMOFCM technique makes use of the Fuzzy C-means clustering framework to build up the cluster formation, and the Spider Monkey Optimization technique to select the Cluster Head (CH)

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Design and Assessment of Bio-Inspired Antennas for Mobile Communication Systems

RF front-end system in the mobile communication system consists of an antenna, filter, and amplifier section. Now a day, there is a need to reduce the size of this RF front-end system. Here the challenge is the reduction in the size of the RF front end without degrading the performance. One way to reduce the size is by reducing the size or area of the antenna. This work proposes and simulates three Bio-inspired microstrip (BIMS) antenna designs. They are flower, Butterfly, and leaf shapes presented based on the perturbation method, Gielis super formula, and modified polar transformation models, respectively. These BIMS antennas were printed on fiberglass laminate (FR4) as substrate with a dielectric constant of εr = 4.4 and a loss tangent of 0.02.

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Design and Analysis of ANFIS Controller for High Accuracy Magnetic Levitation (ML) System

Magnetic Levitation (ML) System is a significant particular research center model for the planning and examination of criticism control frameworks. Due to the usual sensitivity of mass, a solid choppiness powers here between magnets, and also due to the effects of commotion spilling out of the sensor and information channels, the superiority of attractive lift frameworks is dangerous. Thereafter, the design of a control framework for height is a complex issue that must be considered when developing models that are not 100% exact. Elite is the goal, all else being equal, of being better, faster, or much more productive over others.

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Grid-Tied Solar Power Sharing with V2G and G2V Power Exchange with Dual Bridge Integrated Electrical Vehicle

An increasing demand of electrical vehicle technology, renewable power is recommended for the charging of vehicle batteries. The proposed electric vehicle battery charging module has the capability of exchanging power between the grid and the vehicle. For this exchange of power, a DAFB circuit is integrated between the battery and the grid. The grid is induced with a solar-powered plant with multiple PV panels generating high power.This renewable power is either utilized by DAFB charging circuit or injected into the grid

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PV Integrated UPQC with Intelligent Control Techniques for Power Quality Enhancement

The configuration and control of a Unified Power Quality Conditioner (UPQC) coupled with Photovoltaic (PV) system is proposed in this work. By integrating PV to UPQC, the twin advantages of decarbonized clean energy generation in addition to enhanced Power Quality (PQ) is obtained. The series and shunt compensators, which together constitute the UPQC are sequentially interfaced to the common dc-link. In addition to infusing active PV generated power, the UPQC shunt compensator diminishes the load side power quality concerns. The role of a series compensator is to ensure that both the load and source voltages are in-phase perfectly.

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Boundary-Based Hybrid Control Algorithm for Switched Boost Converter Operating in CCM and DCM

It is essential to have enhanced efficiency for the DC-DC converters operating in continuous conduction mode (CCM) and discontinuous conduction mode (DCM). This requires a hybrid controller designed using pulse width modulation (PWM) and pulse frequency modulation (PFM) schemes. This paper fixates on a boundary-based hybrid control algorithm for the second-order DC-DC converter - the switched boost converter. The proposed algorithm works in PWM control scheme for CCM operation, whereas DCM operation uses PFM control scheme. The boundary conditions are defined by the load current, output voltage, and switching frequency.

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Communication Latency and Power Consumption Consequence in Multi-Core Architectures and Improvement Methods

The present electronics world has a lot of dependency on processing devices in the current and future developments. Even non-electronic industries have much data to process and are indirectly dependent on processors. The larger the number of processors incorporated into the architecture, will lower the data handling and processing time; thus, efficiency improves. Hence multi-core processors have become a regular part of the design of processing elements in the electronic industry. The large number of processors incorporated into the system architecture results in difficulty in communicating among them without a deadlock or live lock

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Multiplication free Fast-Adaptive Binary Range Coder using ISW

Data compression is defined as the process of encoding, converting and modifying the bits-structures of data in such a way that reduces less-spaces on the disk. Fast-ABRC, a new context ABRC for compressing the image and video. This paper introduces novel hardware F-ABRC (Fast-adaptive binary range coder) and architecture of VLSI, as it doesn’t have requirement of LUTs (Look-up-Tables) and also it is completely multiplication free. To get the result, we will combine the utilization of simple operation to compute the approximation after encoding every single symbol and the PE (probability estimation) on the basis of ISW (Imaginary Sliding Window) with approximation of the multiplication.

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A Comprehensive Overview on Performance of Cascaded Three Tank Level System using Neural Network Predictive Controller

A Neural Network Predictive Controller (NNPC) is a deep learning-based controller (DLC) that uses artificial neural networks (ANN) to predict the future behavior of a system and accordingly control its outputs. In this paper, an NNPC was used to predict the level of the three cascaded tank and then adjust the inputs as flow rate to maintain the desired level in the tank. A three-tank level system is a system consisting of three interconnected tanks used to store liquids. To achieve the desired level, the NNPC first collects data on system behavior, including inputs and outputs, and uses this data to train the neural network

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Deep Neural Learning based Deming Regression Adder Enhancement on Digital Multiplier for 3D Graphical Applications in VLSI Circuits

This work aims to investigate 3D Technology to provide better performance enhancement for several generations. The three-dimensional integrated circuit allows better integration density, faster on-chip communications, and heterogeneous integration. The goal of this research is to reduce time consumption and power consumption by introducing the Deep Neural Learnt Deming Regression Based Ladner-Fisher Adder Enhancement (DNLDR-LFAE) Technique in VLSI circuits. Input information (carry inputs) is taken for input layer and transmits to hidden layer 1. Deeming regression analysis has performed at hidden layer 1 to pre-process input data and it is send to hidden layer 2

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Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction

The prevalence of cardiovascular diseases (CVD) makes it one of the leading reasons of death worldwide. Reduced mortality rates may result from early detection of CVDs and their potential prevention or amelioration. Machine learning models are a promising method for identifying risk variables. In order to make accurate predictions about cardiovascular illness, we would like to develop a model that makes use of transfer learning. Our proposed model relies on accurate training data, which was generated by careful Data Collecting, Data Pre-processing, and Data Transformation procedures

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Parallel Mirrors Based Marine Predator Optimization Algorithm with Deep Learning Model for Quality and Shelf-Life Prediction of Shrimp

Automatic classification and assessment of shrimp freshness plays a major role in aquaculture industry. Shrimp is one of the highly perishable seafood, because of its flavor and excellent nutritional content. Given the high amount of industrial production, determining the freshness of shrimp quickly and precisely is difficult. Instead of using feature-engineering-based techniques, a novel hybrid classification approach is proposed by combining the strength of convolutional neural networks (CNN) and Marine Predators Algorithm (MPA) for shrimp freshness diagnosis

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Integrated Energy Management System for Microgrid based on Renewable Energy Sources

An effective energy management strategy is crucial to ensure highest system reliability, stability, operation efficiency and cost-effective operation of renewable energy sources based standalone microgrid. This paper presents an efficient energy management system for microgrid incorporated with Photovoltaic system, PMSG based wind turbine and energy storages including battery, fuel cell-Electrolyzer. Implemented hybrid modified invasive weed optimization with perturbed and observed method for PV systems to harvest maximum energy during partial shading condition. A sliding mode controller is implemented for boost converter to work as maximum power point tracker for wind turbine.

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Power Quality Improvement Grid Integrated Photovoltaic with LCL Filter Using DQ Controller

Grid-interactive solar photovoltaic (PV) systems are necessary for the current global scenario owing to their low cost and pollution-free energy source. The integration of PV systems in the power grid needs to be stabilized. To address this, this paper presents a composite controller that can synchronize Photovoltaic (PV) to the Grid, with bidirectional power flow in Grid. The proposed technique is explored with both RL and LCL filters. With grid synchronization of PV power generation, a control loop for power quality disturbance mitigation is simulated.

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Power Quality Enhancement in Solar PV and Battery Integrated UPQC Grid Connected System

This paper discusses a distributed generation system consisting of grid-connected solar PV and a battery-integrated Unified Power Quality Conditioner (UPQC). Embedded in the PV array, the UPQC consists of a series and shunt converter connected back through a common DC link. In this system, power quality problems of clean energy, such as harmonics, voltage drops, ripples, are compensated by injecting active energy into the power grid. The shunt converter is controlled to maintain a constant DC link voltage and harmonic compensation of the load current. The main voltage problem is compensated by a series converter that injects the voltage during sag and swell.

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Congestion Management of Power Systems by Optimal Allocation of FACTS devices using Hybrid Techniques

For system operators, Congestion management is a difficult task as the market’s security and reliability are protected by this methodology. As the magnitude of an electric transmission system is extremely dynamic, limits must be estimated much beforehand, in order to manage the congestion issues at the right time. Flexible AC transmission systems (FACTS) are used to control voltage fluctuation by adjusting the system's real and reactive power. A combination of Improved Remora Optimization (IRO) and Improved Radial Basis Function (IRBF) is used to allocate positions and sizes of the FACTS devices.

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Attack Detection using DL based Feature Selection with Improved Convolutional Neural Network

Decentralized wireless networks that may connect without a central hub are named Mobile Ad-hoc Networks (MANET). Attacks and threats of the most common kind can easily penetrate MANETs. Malware, APTs, and Distributed Denial of Service (DDoS) assaults all work together to make Internet services less reliable and less secure. Existing methods have been created to counter these assaults, but they either need more hardware, result in significant delivery delays, or fall short in other key areas like as energy consumption. This research therefore provides an intelligent agent system that can automatically choose and classify features to identify DDoS assaults.

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Congestion-Free Cluster Formation and Energy Efficient Path Selection in Wireless Sensor Networks using ButPCNN

Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes.

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Performance Analysis of Variable Threshold Voltage (ΔVth) Model of Junction less FinTFET

The work presented in this paper is a variable threshold voltage (ΔVth) model of junction less fin gate tunnel FET (JL FinTFET) in which there is a shift in threshold voltage. As a result, to improve drive current and subthreshold slope among other devices. At the same time, gradually decrease the random dopant fluctuations (RDF) effects on Vth, ambipolar leakage current by using this design. The threshold voltage in the junction less fin gate TFET may be modified using 2D numerical simulations by supplying a voltage to the variable gate. The effects of the threshold voltage change on the device's overall performance investigate.

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Integration of Optical and Free Space Optics Network Architecture for High-Speed Communication in Adverse Weather using suitable Optical Bands

Free Space Optics (FSO) is a highly viable solution for high-speed wireless communication and is widely preferred over radio frequency communication systems because of its faster data transmission, no regulatory requirements and highly secure long-range operations. However, the capacity and availability of FSO optical bands are a significant concern in varying atmospheric conditions. Our objective is to enhance network flexibility and expand wireless network coverage in adverse weather conditions by combining optical and FSO links using optical bands C, S, and O. The study analyzed the performance of a hybrid 4 channels FSO-WDM system with a 100GHz or 0.8 nm channel spacing under different conditions, including adverse weather and varying data rates. An attenuation of 0.25 dB/km was fixed, and the system's performance was analyzed up to 3 km.

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Bimodal-Shared Control Interface for Assisted Mobility Application

The paper presents (Electro-oculography) EOG- (Electroencephalography) EEG- Radio Frequency Identification (RFID) based Bimodal-Shared Control Interface for mobility assistance application by controlling a mobile robotic arm. EOG-EEG based bio-signal based bimodal interface has been used to move the robot following a predefined path to reach at an object placed at initial predefined position (Zone 1). RFID has been used as shared control interface for object identification and for sending trigger signal to gripper arm to pick the object and place it at another predefined position (Zone 2) automatically.

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Multi-image Feature Map-Based Watermarking Techniques Using Transformer

Nowadays, protecting multimedia data is a significant challenge because of the advancement of technology and software. The embedding process heavily relies on watermarking to accomplish multimedia security in terms of content authentication, proof of ownership, and tamper detection. Our objective is to develop an invariant watermark that can survive different signal-processing attacks. We presented a unique hybrid technique (DWT-QR-SWT) and multi-image invariant features generated as a watermark using a Transformer encoder-decoder model. The encoded image features are subsampled using PCA in order to decrease the dimensionality of the watermark image.

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Performance Enhancement of Boost Converter for Solar Panels System using Genetic Algorithm

Solar Panels System (SPS) is a renewable power source with an essential drawback of low output voltage due to the effect of aspects like the intensity of light and ambient temperature. The DC-DC boost converters are significantly used to boost up the SPS voltage under a certain set of conditions. The converter's output voltage and current are unstable, complex, and varied. A three-term controller (proportional, integral, and derivative) is often used because it can control the system’s behavior effectively. The challenge is the selection of the optimum gain parameters of the controller.

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Investigation of Electromagnetic Shielding for Wire Mesh Composite for Aircraft against Lightning Strike

Nowadays, fly-wire is only used for flying-related things. All plane controls depend on electronics, but they also must deal with high-intensity radiated fields. This equipment might need an electromagnetic shield to protect it from outside electromagnetic pollution. The current work aims to develop a mesh around the operating equipment to protect and make it work better. AL6061 was used to create a shield with a metal matrix composite. Here three combinations of Metal Matrix Composite (MMCs) were considered to protect from the high-intensity radiated fields.

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YOLO Based Deep Learning Model for Segmenting the Color Images

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation.

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A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

One of the most important strategies for running and controlling an electric power system is the load frequency controller. LFC can be used to solve a variety of issues, such as when a generating unit is rapidly turned off by protection equipment or when a heavy load is quickly connected or disconnected. When disturbances disrupt the natural power balance, the frequency deviates from what it should be. LFC is in charge of balancing the load and restoring the natural frequency to its proper level. In this case, load frequency control optimization techniques are used in the Multiple Connect Area System to provide reliable and quality operation on frequency and tie line power flow.

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A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

Power quality is the primary issue to be taken into consideration in modern electrical systems, particularly on the distribution side, to protect sensitive loads. Long-term uses can never run out of renewable resources. Connecting STATCOM at the distribution side enhances the power factor by improving the quality of the current waveform. The reactive power range that needs adjustment by utilising PV arrays on the STATCOM's DC side. Increases due to the large rise in terms of the PV power plants' size and capacity. Cascaded H Bridge multilevel topologies can increase the flexibility and effectiveness of PV modules.

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Performance Assessment of Customized LSTM based Deep Learning Model for Predictive Maintenance of Transformer

To improve predictive maintenance of transformers with small DGA datasets, customized LSTM network named C-LSTM is devised to circumvent the boundaries of the standard-LSTM network, which had an increased rate of classification error than conventional machine learning techniques. The study compares the performance of traditional machine learning algorithms with the customized LSTM model using various metrics such as validation accuracy, test accuracy, precision, recall, and F1-score.

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A Fractional Order Tilt Integral Controller Based Load Frequency Control with Dispersed Generation and Electric Vehicle

The elevated level of entrance of decentralized power sources with their Intermittent and volatility gives us a accost task to control load frequency, moreover this quandary is getting worsen up with the Involvement of electric vehicles in the rundown. This paper presents a censorious robust fractional order operative established tilt integral derivative controller (FOTID) for regulating the frequency. To emulate distributed power sources a linearized model of a wind power plant is considered and a small signal model of EV is developed.

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Detection and Segmentation of Meningioma Brain Tumors in MRI brain Images using Curvelet Transform and ANFIS

The detection of abnormal tumor region brain Magnetic Resonance Images (MRI) is complex task due to its similar structures between tumor and its surrounding regions. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classification method-based meningioma brain tumor detection is proposed. The proposed method consists of the following stages as preprocessing, transformation, feature extraction and classifications. The brain MR images are enhanced in preprocessing stage and this spatial domain image is converted into multi resolution image using Curvelet transform.

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Non-invasive and Automatic Identification of Diabetes Using ECG Signals

Diabetes Mellitus is a chronic medical condition in which the body is unable to properly regulate the amount of glucose (a type of sugar) in the blood. It can cause serious consequences like heart disease, nerve damage, and kidney illness. Diabetes causes cardiac autonomic neuropathy, which affects the pattern of electrocardiogram (ECG) signals. ECG measures electrical activity of the hearts. In this paper, the features extraction method is proposed for the classification of diabetic ECG and normal ECG signals.

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Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Wireless sensor networks (WSN) keep developing in recent days concerning the self-covered network, self-healing network, and association of system component circuit selections that enable the implementation process. Wireless sensor network lifetime stabilization is essential to providing a higher quality experience to consumers. The wireless sensor network is associated with classifiers that keep learning the data pattern and further modify the cluster selection to produce dynamic results.

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Design and Analysis of Analog Feedback Communication System with Rayleigh Fading Channel Model

In this work, we mathematically model a wireless Analog feedback communication system (AFCS) using a Rayleigh fading channel. AFCS system is a new research area and has promising applications, especially in low-power devices such as sensors. Compared to AWGN, Rayleigh fading channel more closely models the real wireless environment. In this work, AFCS Rayleigh fading channel is considered in forward transmission while AWGN is considered in the feedback channel.

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Performance Analysis of Energy Efficiency and Security Solutions of Internet of Things Protocols

The scientific and business communities are showing considerable interest in wireless sensor networks (WSN). The availability of low-cost, small-scale components like CPUs, radios, and sensors, which are often combined into a single chip, is crucial. Parallel to the evolution of WSNs, the concepts of the IoT have been evolving in recent years. Wireless communication technologies may play a significant role in the implementation of IoT, despite the fact that IoT does not need or require any particular technology for communication. WSN assisted IoT networks can drive several applications in many industries. The proposed research explores the possibility of enhancing energy efficiency in WSN-assisted IoTN by balancing various challenging sensor network performance metrics.

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A Novel Hybrid Energy Efficient Model using Clustering in Wireless Sensor Networks

An innovative hybrid approach is presented in this paper aimed at rapid predicting the optimum routing path selection in WSNs using the Lagrangian method and Clustering. The main motive of this work proposed is to maximize the clustering productivity, reduce the congestion in routing system and optimize the energy range in the network. By combining the route prediction through clustering and energy flow estimates in the routing protocol design-based algorithm, this will combine both the objectives of rapid route prediction and energy flow estimation

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Development and Application of an Energy Management System for Electric Vehicles Integrated with Multi-input DC-DC Bidirectional Buck-Boost Converter

The rise in environmental pollution, demand for fossil fuels, and higher fuel economy vehicles has raised concerns about the creation of new and efficient transportation vehicles in recent days. These days, most developments in electric vehicles concentrate on making the vehicles more pleasant to ride in. Nonetheless, the emphasis now should be on energy and its most efficient use. To do this, you must give your attention to the origin of the automobile. The answer to this problem may be found in hybrid energy storage systems (HESS). This work is concerned with the design and implementation of an effective energy management system in electric vehicles (EVs) equipped with an active HESS consisting of a battery and a super capacitor via the incorporation of load sharing into this hybridization under a variety of load demand scenarios.

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Design and Control of a Tricycle with a Hybrid Electric Motor Cooling System Powered By Solar Photovoltaics

The power for a standard electric tricycle used for transportation comes from a battery, which can lose power after a certain amount of time. In this regard, the standard tricycle in the proposed concept will have a battery that will be charged by solar panels mounted on a stand on the rear of the tricycle. A solar-based renewable energy source is also used along with the traditional charging mechanism to make a hybrid system. The proposed tricycle is more stable in braking turns because it has a lower center of gravity compared to a bicycle. The proposed tricycle has movement in both directions, i.e., forward and reverse, for disabled persons. The proposed model was validated using the finite element analysis approach in solid work for different points of the frame and different types of loads.

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Novel Low-Pass Filter Structures Using Spur-Lines to Generate Additional Attenuation Poles

We propose two novel low-pass filter (LPF) structures that have generated additional attenuation poles from applying spur-lines to typical open-stub LPFs. The first structure has the spur-lines added to the serial lines, and the second structure has the spur-lines added to the parallel open stub. From the characteristic analyses of the filters with the proposed design, it was confirmed that the length of the spur-line was an important variable for controlling the stopband bandwidth and attenuation depth. The two types of LPFs were fabricated and then validated by the agreement between the theoretical and measured results.

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Power Circuit Design and Analysis of Controller For High-Power Axial Flux PMSM

Designing a high-power controller having high efficiency for permanent magnet motors is a challenge for developers in recent times and very few techniques are available. Design and analysis of power electronic drive for a high-power axial flux permanent magnet synchronous motor is presented in this paper. The motor under consideration here is having two outer stators and single permanent magnet rotor to drive the shaft. Control schemes and methodologies are the major concentration for research. Present paper explains a method to estimate the operational drive parameters and loss calculation according the selected power switches.

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MCS Selection Based on Convolutional Neural Network in TDD System

In this paper, a convolutional neural network (CNN) is proposed for selecting modulation and coding schemes (MCSs) at the time of future transmission in time-division-duplex (TDD) systems. The proposed method estimates the signal-to-noise ratio (SNR) obtained by the average of the equalizer’s output in the orthogonal frequency division multiplexing (OFDM) system and records it to select the most suitable MCS for future transmission. Two methods are proposed: one that directly selects an MCS and one that predicts the SNR first before selecting an MCS. The conventional method commonly used is to select an MCS based on the SNR of the most recently received signal.

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Amplify-and-Forward (AF) Relay Techniques in Wireless SC-FDE Systems to Enhance Diversity Gains

This paper introduces an amplify-and-forward (AF) relaying technique that employs phase dithering and intentional delay within single carrier-frequency domain equalizer (SC-FDE) systems. The proposed relaying technique aims to increase the gain of both frequency diversity and time diversity in slow fading channels. To achieve this, the proposed technique introduces random phase rotation and random intentional delay. The relaying scenario assumes two-hop communication with relaying between the source and destination. It is assumed that many nodes are densely distributed, allowing for many relay nodes to participate in relaying.

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A Diagnostic Study of Content-Based Image Retrieval Technique for Studying the CT Images of Lung Nodules and Prediction of Lung Cancer as a Biometric Tool

Content Based Medical Image Retrieval (CBMIR) can be defined as a digital image search using the contents of the images. CBMIR plays a very important part in medical applications such as retrieving CT images and more accurately diagnosing aberrant lung tissues in CT images. The Content-Based Medical Image Retrieval (CBMIR) method might aid radiotherapists in examining a patient's CT image in order to retrieve comparable pulmonary nodes more precisely by utilizing query nodes. Intending a particular query node, the CBMIR system searches a large chest CT image database for comparable nodes.

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Transmit Antenna Selection Based on SNR prediction in TDD Systems Using Convolutional Neural Network

This paper proposes a method for predicting future signal-to-noise ratio (SNR) in a time-division-duplexing (TDD) mobile communication environments using a convolutional neural network (CNN). The communication system uses multiple receive antennas and transmit using only one or two antennas among them. A CNN model is proposed to predict the SNR at a future transmission time based on past SNRs received from multiple antennas. The probability of reception at a certain is set to 10-100%. In case that SNR cannot be measured due to the absence of reception, linear interpolation is performed using two adjacent recorded SNRs. If even two adjacent SNRs do not exist, the SNR is set to 0dB. Comparing the predicted SNRs at multiple antennas, the antenna with the highest SNR value is selected for future transmission. To verify antenna selection accuracy, computer simulation is conducted.

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Bi-Usage Energy Efficient Techniques (BUEET) for Energizing the Vehicle and Broadcasting the Information in Highway Scenario

The proposed technique BUEET is introduced mainly for two major reasons such as (i) RSU broadcast the emergency messages and the energy status to the vehicles without any interruption. It helps to shake hands with the neighboring node for energy sharing and (ii) RSU boost up the energy efficiency level with the help of energy sharing by the adjacent vehicles. Most of the self-organizing protocols in wireless sensor networks considers only initial energy consumption phase and neglects the maintenance phase of topology. The vehicles are cooperatively interacted to form a reliable network structure. RSU’s are placed in the roadway infrastructure and On-Board Units (OBU’s) are placed in the vehicles, then the communication takes place with the help of these devices.

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Frequency Diversity Using Random Time Delay in Amplify-And-Forward Relay

In a tactical wireless communication environment, it is common for device-to-device communication to occur without a base station, but this can be problematic when the distance between the source and destination is too far. Relays are often used to improve transmission distance and reliability, but amplification-based relaying can result in lower communication performance compared to other methods. This paper proposes a method for obtaining diversity gain through the application of time delay during relaying. The proposed method is compared to a conventional method that uses phase rotation to obtain diversity gain.

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Design of Three-valued Logic Based Adder and Multiplier Circuits using Pseudo N-type CNTFETs

This work presents a novel technique to develop the three-valued logic (TVL) circuit schematics for very large-scale integration (VLSI) applications. The TVL is better alternative technology over the two-valued logic because it provides decreased interconnect connections, fast computation speed and decreases the chip complexity. The TVL based complicated designs such as half-adder and multiplier circuits are designed utilizing the Pseudo N-type carbon nanotube field effect transistors (CNTFETs). The proposed TVL half adder multiplier schematics are developed in HSPICE tool.

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A New Closed Loop Constant v/f Control of Induction Motor with Torque Control Based on DPWM

An easy sensor-less scalar control algorithm is described in this article as a method for controlling the speed of an induction motor. For developing a closed-loop v/f control of the induction motor drive, a torque controller with PI is implemented. The torque command was estimated by utilizing the voltage command, the feedback current, and a torque estimator. Additionally, a torque reference was provided for the Torque PI controller. Considering this, the purpose of this work is to investigate the closed-loop PI-based torque control of an induction motor drive that applies DPWM.

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Performance Estimation of Improved Cooperative Spectrum Sensing under Fading Environment

To deal with spectrum scarcity, Cognitive radio has been considered as a resolving technology. Energy detection(ED) is the most preferable sensing technique due to its lower complexity, ease of working and non-dependency on primary user data requirements. Although having many advantages, ED has some practical limitations like low SNR, shadowing, erroneous reporting channels and multipath fading. Here, a comparative study is done to check the effect of such parameters. And with simulation, it is proven that Cooperative spectrum sensing can reduce the effect of these confines. In this paper, we have also simulated the improved version of ED where decision making is done cooperatively.

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Alpha-Theta Correlations during the Different States of the Brain for a Designed Cognitive Task

Brain oscillations vary due to neurological activities that play an important role in designing a cognitive task. In the proposed study, 27 subjects experimented with different cognitive activities (rest, meditation, and arithmetic) and their alpha and theta bands of frequencies were analyzed. BIOPAC-MP-160 has performed the data acquisition and further processing of the acquired dataset was implemented in EEGLAB. The results illustrated that the cross-frequency correlation (alpha: theta: 1:2) between alpha and theta waves has been enhanced during effortful cognition (arithmetic state).

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Power Quality Improvement using Dual Multilevel Converter for Micro Grid-Connected PV Energy Systems using ANFIS

This paper presents the implementation of dual voltage source inverter (DVSI) approach to improve the microgrid performance by enhancing the power quality. This paper also improves the power quality in photovoltaic (PV) generation interactive microgrids, respectively. The power generated from PV based distributive energy resources (DER) is perfectly applied to the microgrid through the two inverters, thus the nonlinear and unbalance load related problems are compensated. Thus, the power quality problems such as voltage sag, current drops, and power factor, active, and reactive powers are reduced by dual multilevel converter (DMLC).

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Transfer Learning Technique for Covid-19 Screening from CT-Scan: An Empirical Approach

As a result of the Covid-19 pandemic, the field of Medical Sciences has been challenged with new challenges and benchmarks for development. Front line workers are overcoming the Covid-19 challenge with four steps: Screening and Diagnosis, Contact Tracing, Drug and Vaccine Development, and Prediction & Forecasting. Following the above segments carefully can save millions of lives. Artificial Intelligence has proven invaluable in predicting critical factors in many fields. With the ability of AI to process huge databases and conclude with high precision, we are motivated to use AI to screen and diagnose the Covid-19 pandemic.

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A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

The complexity and volume of network traffic has increased significantly due to the emergence of the “Internet of Things” (IoT). The classification accuracy of the network traffic is dependent on the most pertinent features. In this paper, we present a hybrid feature selection method that takes into account the optimization of Particle Swarms (PSO) and Random Forests. The data collected by the security firm, CIC-IDS2017, contains a large number of attacks and traffic instances

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Enhancing Gastric Cancer Lymph Node Detection through DL Analysis of CT Images: A Novel Approach for Improved Diagnosis and Treatment

Although gastric cancer is a prevalent disease worldwide, accurate diagnosis and treatment of this condition depend on the ability to detect the lymph nodes. Recently, the use of Deep learning (DL) techniques combined with CT imaging has led to the development of new tools that can improve the detection of this disease. In this study, we will focus on the use of CNNs, specifically those built on the “MobileNet” and “AlexNet” platforms, to improve the detection of gastric cancer lymph nodes. The study begins with an overview of gastric cancer and discusses the importance of detecting the lymph nodes in the disease management cycle. CT and DL are discussed as potential technologies that can improve the accuracy of this detection.

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A Novel Approach to Cervical Cancer Detection Using Hybrid Stacked Ensemble Models and Feature Selection

Around the world, millions of women are diagnosed with cervical cancer each year. Early detection is very important to produce a better overall quality of life for those diagnosed with the disease and reduce the burden on the healthcare system. In recent years, the field of machine learning (ML) has been developing methods that can improve the accuracy of detecting cervical cancer. This paper presents a new approach to this problem by using a combination of image segmentation and feature extraction techniques. The proposed approach is divided into three phases. The first stage involves image segmentation, which is performed to extract the regions of interest from the input image.

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Cancer Symptoms Detection from Liver CT Images Using Multistage Pre-Processors

Visually cancer is the abnormal pattern with predefined structure could be found in liver Computed Tomography (CT) images. Using deep convolution neural network computation and image processing, this detected abnormal pattern cluster can be classified in different liver issue types. Full size liver CT scan images consisting different body parts, and these are ultrasonic based gray scaled image construction. The primary challenge in the cancer symptoms detection process is to extract the liver area out of image then finding out the actual area of abnormality to conclude whether abnormality is cancer or any other issues on liver.

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High Switching Speed and Low Power Applications of Hetro Junction Double Gate (HJDG) TFET

Tunnel field effect transistor (TFET) technology is unique of the prominent devices in low power applications. The band-to-band tunnel switching mechanism is sets TFET apart from traditional MOSFET technology. It helps to reduce leakage currents. The major advantage is the Sub threshold slope smaller than 60mv/decade. Newer technologies are expected to change the gate, architectures, channel materials and transport mechanisms.

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VMLHST: Development of an Efficient Novel Virtual Reality ML Framework with Haptic Feedbacks for Improving Sports Training Scenarios

This paper presents the development of a novel virtual reality (VR) machine learning (ML) framework that incorporates haptic feedback to improve sports training scenarios. The framework uses You Look Only Once (YoLo) for object detection, and combines it with ensemble learning to analyze the performance of athletes in a simulated environment and provide real-time feedbacks. The system includes haptic feedback devices that are controlled via Grey Wolf Optimization (GWO) to simulate the physical sensation of a real-world sports scenario, allowing athletes to experience the sensation of force, impact, and movements.

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A Diabetic Retinopathy Detection Using Customized Convolutional Neural Network

The disease, Diabetic Retinopathy (DR) causes due to damage to retinal blood vessels in diabetic patients. DR occurs if you have type 1 or 2 diabetes along with high blood sugar. When the retinal blood vessels are damaged, they can become clogged, some of which can block the blood supply to the retina leading to blood loss, these new blood vessels may leak, and the creation of scar tissue can lead to loss of vision. It takes a lot of time and effort to examine and analyse fundus images the old-fashioned way to find differences in how the eyes are shaped. In this modern era, technology has evolved so fleet which has the solution to every problem.

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Anomaly Based Intrusion Detection through Efficient Machine Learning Model

Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets.

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IARMTS: Design of an Interference-Aware Routing Model with Time Synchronization Capabilities for Dense Wireless Sensor Network Deployments

Performance of dense wireless sensor networks is often degraded due to communication interference and time synchronization issues. Existing machine learning & deep learning models that propose bioinspired & pre-emptive packet-analysis solutions for these tasks either have high complexity, or high deployment costs. Moreover, these models cannot be scaled for heterogeneous node & traffic types, which limits their applicability when applied to real-time scenarios. To overcome these issues, this text proposes design of an interference-aware routing model with time synchronization capabilities for dense wireless sensor network deployments.

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Image Forgery Detection Using Integrated Convolution-LSTM (2D) and Convolution (2D)

Digital forensics and computer vision must explore image forgery detection and their related technologies. Image fraud detection is expanding as sophisticated image editing software becomes more accessible. This makes changing photos easier than with the older methods. Convolution LSTM (1D) and Convolution LSTM (2D) + Convolution (2D) are popular deep learning models. We tested them using the public CASIA.2.0 image forgery database. ConvLSTM (2D) and its combination outperformed ConvLSTM (1D) in accuracy, precision, recall, and F1-score. We also provided a related work on image forgery detection models and methods. We also reviewed publicly available datasets used in picture forgery detection research, highlighting their merits and drawbacks.

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Machine Learning Technique for Predicting Location

In the current era of internet and mobile phone usage, the prediction of a person's location at a specific moment has become a subject of great interest among researchers. As a result, there has been a growing focus on developing more effective techniques to accurately identify the precise location of a user at a given instant in time. The quality of GPS data plays a crucial role in obtaining high-quality results. Numerous algorithms are available that leverage user movement patterns and historical data for this purpose. This research presents a location prediction model that incorporates data from multiple users.

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A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Remotely sensed images, their classification and accuracy play a vital role in measuring a country’s scientific growth and technological development. Remote Sensing (RS) can be interpreted as a way of assessing the characteristics of a surface or an entity from a distance. This task of identifying and classifying datasets of RS images can be done using Convolutional Neural Network (CNN). For classifying images of large-scale areas, the traditional CNN approach produces coarse maps. For addressing this issue, Object based CNN method can be used. Classifying images with high spatial resolution can be done effectively using Object based image analysis.

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A New Closed Loop Constant v/f Control of Induction Motor with Torque Control Based on DPWM

An easy sensor-less scalar control algorithm is described in this article as a method for controlling the speed of an induction motor. For developing a closed-loop v/f control of the induction motor drive, a torque controller with PI is implemented. The torque command was estimated by utilizing the voltage command, the feedback current, and a torque estimator. Additionally, a torque reference was provided for the Torque PI controller. Considering this, the purpose of this work is to investigate the closed-loop PI-based torque control of an induction motor drive that applies DPWM.

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Second Harmonic Frequency Adjustment Strategy for Class-E Amplifier Design

The ultrasonic transducers have numerous applications in industries, including medical probes for performing ultrasound scans. One of the significant drawbacks of the ultrasonic transducer is the wastage of a large portion of energy, due to high acoustic impedance, while transmitting ultrasonic waves to the target object. The present study is aimed to investigate the material design of the piezo-composite transducer and improve its performance. Different piezo-composite transducers were simulated in the COMSOL environment by varying input parameters, and three key performance indicators (KPI) were calculated. Many constraint-based multivariable optimization algorithms have been used to maximize the KPIs. A set of parameters, such as Sensitivity and Fractional Bandwidth, have been found to increase the performance of piezo-composite transducer model and its overall efficiency. This study is intended to impinge unidirectional property to the transducer which is found to be beneficial in more accurate medical as well as structural reports and cost savings.

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Islanding Detection in Distribution Generation using Active Method

There are two techniques to ensure that renewable energy systems run continuously: on-grid and off-grid. In the first case the system can be managed in a network, in the second case it can be managed in a micro grid or island mode. Islanding means when a distributed generator (DG) keeps running even after there is no longer any external electricity. This situation can be dangerous for utility systems because it prevents equipment from connecting properly.

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Implementation of Turbo Trellis Coding Modulation Scheme for Fading Channel

In the context of data communication, encountering fading channels can lead to errors occurring at the receiving end due to multipath propagation. To address this challenge, researchers have persistently worked towards developing Error Correction Schemes that effectively manage these errors and guarantee error-free data reception for the receiver. One area of focus lies in the implementation of Forward Error Correction Schemes directly at the transmitter end. Nonetheless, integrating error correction coding using these schemes comes with the drawback of increased bandwidth requirements since additional bits must be included to facilitate error correction. Fortunately, there exists a coding scheme known as Trellis Coded Modulation (TCM), which specifically tackles this concern.

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Image segmentation in Diagnosing the Ground Bud Necrosis Virus in Tomatoes using K-Means Clustering

Early-stage fruit disease detection will ensure the natural product quality for the organic agriculture business. The potential of using K-Means segmentation for diagnosing tomatoes fruit disease was intended to be explored by this proposed method. The main goal of paper is to increase classification accuracy by locating tomatoes with Ground Bud Necrosis Virus in Tomatoes disease using an image segmentation approach. The K-means clustering algorithm is intended to boost segmentation effectiveness.

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An Efficient Hybrid Analysis to Improve Data Rate Signal Transmission in Cognitive Radio Networks Using Multi- Hop

Spectrum scarcity problems can be resolved with the emerging communiqué technologies known as cognitive radio (CR). Cognitive radio networks (CRNs) will give mobile users greater bandwidth via wirelessly heterogeneity design and dynamic spectrum acquisition methods. The Cognitive Radio Mobile Ad-Hoc Network (CR-MANET) idea of Adaptive Routing a new network paradigm may be realized by using the functions of spectrum management to overcome such difficulties. Secondary users (SUs) have the freedom to opportunistically explore and make use of the open spaces on licensed channels. When a primary user (PU) interferes with a licensed channel, this forces the SU to leave it and switch to an open channel.

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Design and Implementation of a Bootstrap-based Sample and Hold Circuit for SAR ADC Applications

The resolution and conversion speed of an Analog to Digital converter (ADCs) strongly depends on how efficiently Sampling and Hold (S&H) circuit handles the amplitude skewing of the input analog signal. In this article, a novel S&H circuit has been proposed to handle the errors produced because of amplitude skewing. This circuit has two different paths for sampling and holds process and avoids the non-ideal effects seen in most of the recent literature. In portable applications, the restrictions on the available power and the importance of the quality of digital data are taken as a challenge. To make SAR-ADC more power efficient, all blocks should be designed with low-power techniques. Here, the sample and hold block need to be designed to the optimized power level, operate supply of 3.3V, implemented with SCL 0.18µm process, operating at a sampling rate of 10MHz with the power of 0.425mW.

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A Solution to VLSI: Digital Circuits Design in Quantum Dot Cellular Automata Technology

Quantum Dot Cellular Automata is a Nano device efficient than other devices in nanotechnology for the last two decades. It is beneficial over Complementary Metal Oxide Semiconductor technology like high speed, low energy dissipation, high device density and high computation efficiency. To achieve further optimization different methods like simplifications in Boolean expressions, tile method, clocking scheme, cell placement, cell arrangement, novel input techniques, etc., are in use. These methods improve the performance metrics in terms of QCA Cells, total circuit area, delay in output, power consumption, and coplanar or multilayer layout. This paper is about the novel NOT gate layout designed with efficient parameters compared to existing NOT gates except area parameters with analysis and XOR gate and multiplexer circuits. The novel gate provides an improvement of 55% in the number of cells, polarization raised by 0.33, and an 80.77% improvement in total area. These circuits illustrate further scope in QCA circuit design efficiently. XOR circuit shows area reduction up to 0.006 μm2 with 0.5 clock cycle delay. Further optimization in XOR parameters and with this novel NOT gate researchers can optimize parameters to bring revolution and digitalization.

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A New Soft Computing Fuzzy Logic Frequency Regulation Scheme for Two Area Hybrid Power Systems

Modern renewable energy power system designs provide significant application benefits, but they also produce losses. The total generation, total load demand, and system losses must be balanced in order for this structured power system to operate reliably. The actual and reactive power balances are disturbed as a result of changes in load demand. System frequency and tie line interchange power deviate from their planned values as a result of this. A high system frequency deviation can cause the system to crash. In that case, multiple connect area systems use intelligent load frequency control techniques to deliver dependable and high-quality frequency and tie line power flow. Here, a standalone hybrid power system is taken into consideration, with generated power and frequency being controlled intelligently. In addition to the unpredictable nature of the wind, frequent adjustments in the load profile can produce sizeable and detrimental power variations. The output power of such renewable sources may fluctuate to the point that it causes significant frequency and voltage changes in the grid. An intelligent approach recently proposed to address the load frequency control (LFC) issue of an interconnected power system is known as fuzzy logic PID controller (FLPIDC). Standard proportional integral derivative (PID) controllers are used to control each section of the system.

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Optimal Power Flow for Distribution System using Gradient-Based Optimizer

In the distribution network, DG penetration increases prominently, and has altered the nature of the distribution network into an active and passive network. DISCOMs/DSOs are incorporating all kinds of DGs, including non-renewables and renewables now a day. If DGs are planned and controlled adequately, then it improves voltage deviation, reduces active power loss, and leads to the economic operation of the active distribution network. Efficient operation of the distribution network can be achieved by solving optimal power flow. In this work, optimal power flow (OPF) for a modified IEEE-69 bus distribution network with DGs is formulated and solved using Gradient Based Optimizer (GBO) in MATLAB 2021a. OPF is solved with objectives to minimize fuel cost, voltage profile improvement, and active power losses. The performance of GBO is compared with other state of art algorithms (PSO, ABC, GWO, and JANA). Performance analysis proves the efficacy and capability to solve real-world problems of GBO over other state of art algorithms.

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A Combination of Appropriate Placement and size of Multiple FACTS Controllers to reduce Voltage Sag and Swell

Today's power system is going through a power quality crisis as a result of rising power demand and an increase in industrial facilities. The forms must be pure sinusoidal and harmonic-free, and the power source must always be reachable within voltage and frequency restrictions. This study uses numerous FACTS controllers in a radial distribution system to handle power quality concerns. Placement of FACTs controllers in the distribution system under various load conditions presents the biggest challenge. The system is run while deploying single and multiple FACTS controllers at the critical buses in order to avoid conflicts. This paper presents on the installation of a DSTATCOM, Integrated Dynamic Voltage Restorer-Ultra Capacitor (IDVR-UC), and UPQC to reduce power quality issues for conventional IEEE-33 bus distribution systems.

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Smart Energy Meets Smart Security: A Comprehensive Review of AI Applications in Cybersecurity for Renewable Energy Systems

The rapid adoption of renewable energy systems has brought forth a new set of cybersecurity challenges that require innovative solutions. In this context, artificial intelligence (AI) has emerged as a promising approach to tackle these challenges. This paper provides a comprehensive review of more than 19 studies that investigate the applications of AI in cybersecurity for renewable energy systems. By analyzing these studies, a range of opportunities and challenges associated with the integration of AI in this domain are identified. Notably, the findings indicate that over 75% of the studies acknowledge the significant potential of AI in enhancing the security of renewable energy systems. Among the various AI techniques employed, machine learning emerges as the most extensively utilized method, demonstrating an impressive detection rate of 85% and a false positive rate below 5%. However, certain challenges persist, including the limited availability of relevant data and concerns regarding the interpretability of AI models. To address these challenges, this paper concludes by providing recommendations for future research directions in this field, aiming to drive advancements in the intersection of smart energy and smart security.

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VLSI Implementation of Hybrid Memristor Based Logic Gates

Practical memristors have gained attention from researchers and scientists due to their potential use in a variety of electronic circuits and devices. In our paper, a hybrid Memristor-CMOS (MeMOS) logic circuit was designed and its transient response was analyzed. This circuit, which uses a N-type metal oxide semiconductor (NMOS), and P-type metal oxide semiconductor (PMOS) transistors, Operational amplifiers (OPAMPs), resistors, capacitors and multipliers replicate memristor characteristics. To facilitate the development of real memristor circuit applications, a memristor emulator is utilized for breadboard experiments. This emulator can be connected in a variety of configurations, including serial, parallel, or a combination of both, with identical or opposite polarities. By simply changing the connection, the emulator can be switched between decremental and incremental configurations. In our paper, we implemented AND logic using MeMOS. PSpice simulation of the proposed emulator have been demonstrated for TiO2 memristor model.

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Optimization of Microstructure Patterning for Flexible Bioelectronics Application

Recent advancements in flexible electronics and wearable sensors have given biomedical technology a new edge overcoming the limitations of traditional rigid silicon-based electronics. Furthermore, high flexibility of these wearable sensors enables it to conformally sit over any uneven surface helping in accurate determination of any physical, chemical, or physiological parameter associate with the surface. Conventionally expensive micro/nano photolithography techniques under strict clean room conditions are used for the development of these flexible and wearable biomedical sensors with high degree of accuracy and sensitivity. However, the developed wearable sensors need not only be extremely sensitive, but also cost effective for its successful usage. To address this, the present work discusses the use of a photo-patternable UV sheet for realization of micro patterns over flexible copper cladded surface eliminating the need of costly clean room facilities. It demonstrates the standardization of various design geometries using the photo-patternable UV sheet over the flexible surface similar to photolithography process and involves optimization of the exposure timing of the UV sheets and their development time towards various design patterns over different thick film metal surfaces. Finally, patterned micro devices like micro-electrodes were successfully realized using the above process to ascertain its efficacy.

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Demonstration of an Intelligent and Efficient Smart Monitoring System for Train Track By using Arduino

In Indian railway, the smart monitoring system for the train also train track is a significant aspect to prevent accidents. Indian railway system is underdeveloped in terms of smart monitoring of the train when compared with the other developed countries. Using the smart monitoring system for train, the deterioration of the railway track could be identified and secondly, accident between two trains could be prevented, thirdly any obstacle present in railway track, could be find and removed, two coaches of the train getting disconnected during the movement of the train due to manufacturing mistakes could also be detected. It helps to detect fire in the particular coach of train. Smart monitoring of the train can be achieved by the help of some semiconductor devices such as laser, laser camera and photodiode is used. Smart monitoring system of the railway could help to monitor the train and its track in an efficient way it could be implemented in Indian railway to avoid accident and extricate people’s life.

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Disease Detection and Diagnosis of Agricultural Plant Leaf Using Machine Learning

Agriculture and allied activities still continue to be one of the major occupations in world. Various modern methods and inventions have been incorporated to make it more efficient and successful. One of the main problems the farmers are facing are plant diseases. This can affect the entire yield of a season, so to tackle that problem we are proposing a ResNet based Convolutional neural network model which can detect the various disease in plants in early stage itself. For this purpose, ‘New plant village’ dataset to train and test the model. The proposed Resnet based approach has achieved high accuracy in detecting diseases as well as suggesting a proper solution and possible causes for a plant disease.

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Designing of Tunnel FET and FinFET using Sentaurus TCAD and Finding their Characteristics

In this paper, a FinFET and Tunnel FET (TFET) are designed and implemented using Sentaurus TCAD. Due to numerous advantages, the TFET and FinFET have been proposed as a possible alternative to the conventional metal oxide semiconductor FET (MOSFET). A phenomenal performance-has been achieved using FinFET technology up to a 7 nm feature size. A detailed observation is made on FinFET and TFET regarding various effects such as short channel effects, quantum tunneling effect and characteristics like electric field, voltage and current, on-current, doping concentrations, energy band diagrams etc. FinFET technology can be used for designing different low power CMOS digital circuits and memory-based circuits. On the contrary, TFET based synthesized circuits are known for their high sensitivity, for which they are suitable for sensing applications, especially biosensors.

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Hand Gesture Recognition System based on 60 GHz FMCW Radar and Deep Neural Network

In The proposed study provides a novel technique for recognizing hand gestures that use a combination of Deep Convolutional Neural Networks (DCNN) and 60 GHz Frequency Modulated Continuous Wave (FMCW) radar. The motion of a Human's hand is detected using the FMCW radar, and the various gestures are classified using the DCNN. Motion detection and frequency analysis are two techniques that the suggested system combines. The basis of the capability of motion detection in FMCW radars' is to recognize the Doppler shift in the received signal brought on by the target's motion. To properly identify the hand motions, the presented technique combines these two techniques. The system is analyzed using a collection of hand gesture photos, and the outcomes are analyzed with those of other hand gesture recognition systems which are already in use. A dataset of five different hand gestures is used to examine the proposed system. According to the experimental data, the suggested system can recognize gestures with an accuracy of 96.5%, showing its potential as a productive gesture recognition system. Additionally, the suggested system has a processing time of 100 ms and can run in real time. The outcomes also demonstrate the proposed system's resistance to noise and its ability to recognize gestures in a variety of configurations. For gesture detection applications in virtual reality and augmented reality systems, this research offers a promising approach.

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Power Optimized VLSI Architecture of Distributed Arithmetic Based Block LMS Adaptive Filter

In this paper, we are presenting a power-efficient Distributed Arithmetic (DA) based Block Least Mean Square (BLMS) Adaptive Digital Filter (ADF). The proposed DA BLMS architecture proposes a shared area-efficient Multiplier Accumulate Block that calculates both the partial filter products and the weight increment terms in the same module. It also uses Multiplexers (MUX) and Demultiplexers (DEMUX) which passes only L out of N inputs, where N and L are the filter length and chosen block size respectively, into the MAC thus helping in achieving the DA functionality along with reduced power consumption. Also, efficient truncation of the obtained error and weight update terms is performed by being able to select the non-zero-bit part of the signal to be fed back. The entire architecture is driven by a single slow clock which reduces the power consumption of the device further. On comparing with the best existing DA BLMS Structures, the proposed architecture uses 15% lesser power, 14% lesser EPS according to ASIC Synthesis, and for a filter length of N=16 and a block size of L=4 respectively.

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A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces

In advanced driver assistance system detection of road surfaces is an important task. Few algorithms have been proposed in past to detect the road surfaces based on intensities. However, problem arises in detection process is due to the presence of shadows or wet road surfaces. Here we have proposed a novel algorithm for detection of shadows with the help of machine learning approaches. Initially shadow is being detected with the help of a threshold-based approach followed by windowing-based method. The detected shadow region gets confirmed with the help of a set of features and classifier. The detected shadow or wet pixels are in painted to obtain set of pixels without shadow for road classification problems. The simplicity and accuracy of the algorithm makes it robust and can be used as a part of road surface detection algorithm.

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A Machine Learning Approach for Detection and Suppression of Shadow or Wet Road Surfaces

Electrical demand, which makes up a large share of the overall power market, agriculture at the top of the list of priorities. To provide end users with a dependable and high-quality supply via various feeders and renewable energy sources, distribution generations are now being developed. In recent years, solar PV systems have been used to meet the demands of numerous applications, including boosting the efficiency of distribution networks. This paper presents the system with effective optimization method like Artificial Eco-System based Optimization Technique for identification of the best location to install distribution generation and the optimum size to minimize feeder losses. To meet service expectations, the integration of a solar PV system is swapped out for a solar tree in this suggested work. A 28-bus Indian agriculture feeder is considered for better understanding the proposed algorithm. MATLAB software is used for implementing the proposed optimization technique and CREO-2.0 is used for designing the 3-dimensional solar PV tree.

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Addressing Power Loss and Voltage Profile Issues in Electrical Distribution Systems: A Novel Approach Using Polar Bear Gradient-Based Optimization

Energy is an essential commodity for everyone, with electrical energy being the most preferred form. Unfortunately, non-renewable energy resources are gradually depleting, and renewable energy sources take several years to establish. To mitigate this problem, technology has shifted from non-renewable energy sources to electrical devices and machines, including household appliances like washing machines and air conditioners. However, the generation of electricity is still inadequate to meet the growing demand. This leads to two major problems: high power loss and poor voltage profile, making it difficult for power distribution companies to ensure a consistent and reliable power supply. This paper aims to address the reduction and minimization of power losses by adjusting distribution side transformer tap settings using the polar bear gradient-based optimization. The proposed approach uses the 14-bus system as a reference and calculates losses for this system using the backward-forward sweeping technique. The results are compared with standard PSO algorithm; the proposed strategy shows superior results.

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An Adaptive Grid Search Based Efficient Ensemble Model for Covid-19 Classification in Chest X-Ray Scans

Covid has resulted in millions of deaths worldwide, making it crucial to develop fast and safe diagnostic methods to control its spread. Chest X-Ray imaging can diagnose pulmonary diseases, including Covid. Most research studies have developed single convolution neural network models ignoring the advantage of combining different models. An ensemble model has higher predictive accuracy and reduces the generalization error of prediction. We employed an ensemble of Multi Deep Neural Networks models for Covid.19 classification in chest X-Ray scans using Multiclass classification (Covid, Pneumonia, and Normal). We improved the accuracy by identifying the best parameters using the sklean Grid search technique and implementing it with the Optimized Weight Average Ensemble Model, which allows multiple models to predict. Our ensemble model has achieved 95.26% accuracy in classifying the X-Ray images; it demonstrates potential in ensemble models for diagnosis using Radiography images.

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Comprehensive Analysis of IoT with Artificial Intelligence to Predictive Maintenance Optimization for Indian Shipbuilding

The extensive review of the literature evaluation on predictive maintenance (PdM) in this work focuses on system designs, goals, and methodologies. In the business world, any equipment or system failures or unscheduled downtime would negatively affect or stop an organization's key operations, possibly incurring heavy fines and irreparable reputational damage. Traditional maintenance methods now in use are plagued by a variety of limitations and preconceptions, including expensive preventive maintenance costs, insufficient or incorrect mathematical deterioration procedures, and manual feature extraction. The PdM maintenance framework is suggested as a new method of maintenance framework to prevent any damage only after the analytical analysis shows specific malfunctions or breakdowns, which is in line with the growth of digital building and the advancement of the Internet of Things (IoT), and Artificial Intelligence (AI), and so on. We also present an overview of the three main types of fault diagnosis and prognosis methods used in PdM mechanisms: scientific, conventional Machine Learning (ML), and deep learning (DL). While offering a thorough assessment of DL-dependent techniques, we make a quick overview of the knowledge-based and conventional ML-dependent strategies used in various components or systems. Eventually, significant possibilities for further study are discussed.

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Deep Learning Method of Predicting MANET Lifetime Using Graph Adversarial Network Routing

The prominence of mobile ad-hoc networks (MANETs) is on the rise. Within the domain of machine learning, a specialized subset known as deep learning (DL) employs diverse methodologies, each providing unique interpretations of the data it processes. In existing system the vulnerabilities of MANETs to security threats stem from factors such as node mobility, the potential for MANETs to provide economical solutions to real-world communication challenges, decentralized management, and constrained bandwidth. The efficacy of encryption and authentication methods in safeguarding MANETs encounters limitations. Intelligence will be the future development direction of network adaptive optimization technology in response to the increasingly complex mobile communication network. Data from mobile communication is a crucial part of the future information society. This paper propose adaptive optimization scheme , employs a machine learning algorithm that is capable of realizing the optimal parameter configuration and coordinating various optimization objectives in response to changes in state and environment. The coordination and advancement of social, versatile and area administrations make the customary informal organization easily change to portable correspondence organization. Creation of a system that can learn some rules from data and apply them to subsequent data processing is the research objective. This paper examines the machine learning-based algorithm for big data analysis and effectively addresses the issue of communication network data using graph theory and the experimental result shows higher lifetime prediction accuracy compare to previous system.

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Modified E-Shape Rectangular Microstrip Patch Antenna with DGS for Wireless Communication

A modified E-shape dual bands rectangular microstrip patch antenna for wireless applications is presented in this paper. An E-slot Microstrip patch antenna with a defective ground structure method has been proposed and getting two bands at 1.9 GHz and 2.89 GHz with S11 -10dB. Defective ground structures provide a maximum gain and low insertion loss i.e., a gain of 3.16 dB, voltage standing wave ratio less than 2, and insertion loss less than -10 dB for both bands. The size of the antenna is 46.83mm x 38.41mm x 1.676mm, which is compact in term of size. The dual band microstrip patch antenna exhibits low cost. The simulation's outcome closely resembles the actual printed antenna and applicable for WiMAX application. The antenna was designed using the Computer Simulation Technology (CST) software and printed on FR-4 substrate.

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A Novel Black Widow Optimized Controller Approach for Automatic Generation Control in Modern Hybrid Power Systems

This research paper demonstrates an application of the Black Widow Optimization (BWO) approach to address the issue of load-frequency control (LFC) in networked power systems. BWO is an innovative metaheuristic method that quickly suggests technique is initially evaluated on a non-reheat thermal-thermal (NRTT) power system spanning two areas of interconnection, and then it is applied to two different actual power systems: (a) a two-area thermal-thermal considering Generation Rate Constraint (GRC); and (b) a two-area having thermal, hydro, wind, solar, and gas systems. The BWO method uses two fitness functions based on integral time multiplied absolute error (ITAE) and integral square error (ISE) to optimize controller gains. The suggested BWO algorithm's performance has been compared to that of existing meta-heuristic optimization methods, such as grey wolf optimization (GWO), comprehensive learning particle swarm optimization (CLPSO), and an ensemble of parameters in differential evolution (EPSDE). The simulation results show that BWO's tuning skills are better than other population-based planning methods like CLPSO, EPSDE, and GWO. The ITAE value is enhanced by 33.28% (GWO), 40.28% (EPSDE), and 43.27% (CLPSO) when the BWO algorithm is used in conjunction with the PID Controller for thermal system.

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Feature Fusion of Time-frequency and Deep Learning Features for Epileptic Seizure Detection using EEG Signals

A persistent brain's neurological state is epilepsy, characterised by recurring seizure. Brain electrical activity is measured using EEG signals, which can be used to detect and diagnose significant brain problems such as Epilepsy, Autism, Alzheimer’s etc. However, manual EEG data processing is time-consuming, requires highly skilled clinicians, and is associated with low inter-rater reliability (IRA). A computer-aided diagnosis approach for epileptic seizure detection from multichannel EEG recordings by fusing the time-frequency features and the deep learning features extracted from Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model using canonical correlation analysis (CCA) method is provided in this study. Deep Learning features are extracted using CNN-GRU layers, motivated by recent advancements in image classification and optimised for use with EEG data. We have also extracted time-frequency features such as spectral entropies and Sub Band energies from Empirical mode decomposition (EMD) and Hilbert Marginal Spectrum (HMS). We used CHBMIT dataset to carry out the results and showed that the method proposed for fusing the time-frequency features and deep learning has given better performance.

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Estimation of Common Mode noise and Differential Mode noise generated by DC-DC Power Converters

The study contains a review of the body of knowledge regarding differential mode (DM) and common mode (CM)noise and how they affect power converter performance. With an emphasis on practical application, this work seeks to give an estimation of differential mode (DM) and common mode (CM) noise for cutting-edge DC-DC power converters such as Zeta converters, Single Ended Primary Inductance Converters (SEPIC), and Cuk converters. Active noise separators and Differential mode noise separators are used as a measurement technique to quantify DM and CM noise, considering a number of variables including input voltage, output voltage, load current, and switching frequency. By using filtering techniques, DM and CM noise can be reduced. Both CM noise and DM noise are created by the Zeta converter at 114 dBµV and 108 dBµV, respectively. CM noise from the SEPIC converter is 119 dBµV, and DM noise is 114 dBµV. With values of CM noise 98 dBµV and DM noise 106 dBµV, Cuk converter produces less noise when compared to Zeta and SEPIC converter. The results show that power converters can generate DM and CM noise, and that this noise is over the Comité International Special des Perturbations Radioélectriques [CISPR] limit line. The conducted emission range for various electronic devices is provided by this standard. This study provides useful insights for power converter designers and engineers to optimize the performance of their systems in practical applications.

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Advancements in Machine Learning-Based Face Mask Detection: A Review of Methods and Challenges

Wearing face masks is crucial in various environments, particularly where there is high potential of viral transmission. Proper wearing of face masks always is important in hospitals and healthcare facilities where the risk of transmission of different contagious diseases is very high. The COVID-19 pandemic has been recognized as a global health crisis, exerting deep impacts on various sectors such as industry, economy, public transportation, education, and residential domains. This rapidly spreading virus has created considerable public health risks, resulting in serious health consequences and fatalities. Wearing face masks in public locations and crowded regions has been identified as one of the most effective preventive methods for reducing viral transmission. Using powerful face mask detection systems in such contexts can thus significantly improve infection control efforts while protecting the health and well-being of healthcare personnel, patients, and visitors. In this paper, we present a comprehensive review of recent advancements in machine learning techniques applied to face mask identification. The existing approaches in this sector can be broadly categorized into three main groups: mask/no mask detection approaches, proper/improper mask detection approaches, and human identification through masked faces approaches. We discuss the advantages and limitations associated with each approach. Further, we explore into the technical challenges encountered in this field. Through this study, we aim to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art machine learning techniques for face mask detection.

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Performance Enhancement of CNFET-based Approximate Compressor for Error Resilient Image Processing

The approximate computing has emerged as an appealing approach to minimize energy consumption. By implementing inexact circuits at the transistor level, significant enhancements in various performance metrics such as power consumption, delay, energy, and area can be achieved. Consequently, researchers worldwide have been actively exploring the application of inexact techniques in circuit design. This paper introduces a novel technique for designing low-power digital circuits called extremely low power modified gate diffusion input (ELP-MGDI).

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Robust medical image watermarking in frequency domain

Protecting patient information in medical image watermarking poses a significant challenge, especially when traditional methods like the Arnold transform prove inadequate in ensuring security. This paper introduces a novel approach within the Discrete Wavelet Transform (DWT) domain to address this issue effectively. By employing the Advanced Encryption Standard (AES), the security and robustness of the system are greatly enhanced through the encryption of both the medical image and patient data.

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A Novel Buffer Packet Delivery Strategy for High Throughput and Better Health (HTBH) Method in Wireless Sensor Networks

There is massive call for the Packet Sender Device Network (PSDN) primarily based on tracking of areas, figuring out the consequences of climate, detection of enemy vehicles. The PSDN could have useful Packet Sender Devices (PSD’s) which can study the vicinity and then send the data from initial to receiver PSD. There are numerous constraints which have restrictions on the feature like battery, memory, and range. There is hierarchical community wherein the PSDs are spread on more than one area with every vicinity having their very own PSD’s whilst communique has to manifest among PSDs of various areas then it requires chief PSD in every vicinity which have to be elected primarily based on higher battery degree, distance to base station in addition to mobility of the PSD over a duration of time.

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A Novel Modified Energy and Throughput Efficient LOYAL UN-Supervised LEACH Method for Wireless Sensor Networks

The work presented in this paper concerns the design of an integrated flyback DC-DC micro-converter operating at high frequencies. The flyback converter consists of only one transformer. The integrated micro-transformer in the flyback micro-converter is composed of two planar stacked coils with spiral octagonal geometry. Basing on Mohan’s method, the geometrical parameters are evaluated.

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Oral Tumor Segmentation and Detection using Clustering and Morphological Process

The Doubly Fed Induction Generators (DFIG) based wind turbine is fed with maximum power point tracking is presented in this paper in proposed technique the proportional coefficient tuned adaptively as per wind changes and compare with traditional approaches. This novel method uses three control laws to adjust the proportional gain adaptively to wind speed variations.

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Switched Capacitor-Based Bidirectional Power Converter with Enhanced Voltage Boost and Reduced Switching Strain for Electric Vehicle Applications

This research work presents an improved design of a bidirectional converter for EVs, specifically focusing on its buck and boost operations. The proposed design incorporates a switched capacitor-based double switch converter, which offers enhanced performance compared to conventional converters.

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Switched Capacitor-Based Bidirectional Power Converter with Enhanced Voltage Boost and Reduced Switching Strain for Electric Vehicle Applications

In wireless communication, the Ultrawide Band (UWB) is a technique for achieving a higher data rate, low power consumption, and less complexity. Impulse Radio - Ultra-Wide Band (IR-UWB) uses the baseband signal technique, reducing circuit complexity and power consumption. This work proposes an IR-UWB transmitter block with low power and tunable bandwidth that meets the UWB regulations of LRP (Low-Rate Pulse) UWB.

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Futuristic Energy Management Solution: Fuzzy logic controller-Enhanced Hybrid Storage for Electric Vehicles with Batteries and Super Capacitors

The core focus of this study was directed towards devising an energy management strategy tailored for hybrid storage systems (HSS) within electric vehicles, with the prime objective of enhancing the longevity of the battery cycle. The batteries employed in electric vehicles (EVs) are prone to expedited deterioration resulting from harsh charging/discharging cycles and the substantial power surges experienced during acceleration and deceleration phases.

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Analysis on Rapid Charging for Electrified Transportation Systems

Background: To improve system resilience when charging electric vehicles, a new control mechanism for converters that convert voltage from sources in micro-grids is presented. Methods: This deals with an evolving continuous current and stable voltage charging method for electric automobiles (EVs) with the objectives of speedy charging, constant voltage stability, deviation from voltage reduction, and cost reduction.

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Lifting Wavelets with OGS for Doppler Profile Estimation

This article discusses the second-generation wavelet transform concept and technique and its application to the noise removal problem of MST radar data. Located near Gadanki in Andhra Pradesh, India, the MST radar is collecting data on climate change. To obtain weather data, the signal collected by the radar needs to be analyzed, which usually requires power spectrum estimation. Most parametric and non-parametric methods cannot predict Doppler at an altitude above 14 KM, which makes to search for introduction of new denoising methods.

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A Symmetric Multi-Level Cascaded H-Bridge Inverter for Renewable Energy Integration

The advanced multi-level cascaded H-Bridge inverter system described in this paper is novel and intended for effective integration of renewable energy sources. Phase-displacement pulse width modulation (PD-PWM) control has been employed in the proposed five-level topology to produce output voltage with better quality. The system incorporates proficient filtering methods with a low total harmonic distortion (THD) desired outcome. With a stable output of 230 V at 50 Hz and a 2.3 kW capacity, the inverter system has been satisfied the exacting IEEE 519 standards for power quality.

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A Novel Passive Islanding Detection Method for Distributed Generation

Off-grid and On-grid are two technologies that allow renewable energy sources to run continuously. The system can be networked in the first scenario, and it can operate independently or as a microgrid in the second. The decentralised generator (DG) can run in island mode even if there isn't an external power supply accessible. This circumstance may prohibit the equipment from correctly joining, endangering the auxiliary system. In order to find island patterns at particular times, this research suggests a passive method.

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An Evaluation of the Signal to Noise Ratio (SNR) of Next Generation Wireless Communication Systems using Large Intelligent Surfaces: Deep Learning Approach

The existing data rate must be greatly increased in order to support the numerous applications of the next generation communication systems. Using Large Intelligent Surfaces (LIS), which are a panel with mounted reflective components, is one way to address this problem. Their primary function is to divert the electromagnetic signal to the intended user. As a result, the received signal's strength and reception quality both improve, improving the Quality of Service (QoS). Machine Learning algorithms have been used to implement LIS in a number of ways, including channel estimation and the calculation of phase shifts (discrete), to mention a few.

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Interconnection Study in Utilizing of Solar Energy for 150 MW Photovoltaic Power Generation through 150 kV Transmission Line

The interconnection of utility-scale photovoltaic (PV) power plants with the electric grid is a crucial factor that requires comprehensive analysis and assessment. The focus of this research article is on a specific photovoltaic (PV) power plant that is planned for construction in the X Power System located in Indonesia which has 150 MW capacity which has intermittent behavior, experiencing fluctuations in power generation based on the availability of sunlight and the cloud movement. The objective of this paper is to explore the feasibility, technical prerequisites, and potential solutions for the successful integration of the PV power plant into the existing power system.

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Analysis Optimization and Comparison to Detect Failures in the Squirrel-Cage Rotor using High-Level Wavelets

The methods and tools used for signal analysis extracted from the induction motors, such as the motor current signature analysis (MCSA) used for data collection on a non-invasive basis, the multi-resolution analysis (MRA) and discrete wavelet transform (DWT), are efficient tools for the signal analysis at different levels or resolutions, these tools have been applied together to improve detection of failures in the rotor of induction motors in condition of no-load. This work focuses on the study of rotor cage end ring, in a condition with lower-load or no-load where uncertainty predominates, this area of study is complicated to analyze correctly with conventional methods, but in these circumstances, the analysis using TDW has better performance.

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Black Widow Optimization for Power System Load Frequency Control: A Comparative Study

This research paper mainly engrossed on developing a suitable novel tuning methodology named Black Widow Optimization (BWO) Algorithm for power system optimization problems. Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR), two of the most important control systems in the power system arena, are employed as test systems to assess the efficiency of the suggested BWO approach. Various analyses, such as transient analysis, are employed to evaluate the efficiency of the suggested BWO approach in LFC and AVR systems. robustness analysis and convergence analysis.

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Design and Implementation of a Two-Frequency Power Amplifier without Interference for Wireless Communication

In this research paper, we will delve into a type of circuit known as a power amplifier. This circuit is designed to operate at two frequencies, allowing it to perform tasks at each frequency. The main goals of the design are to separate signals and ensure proper operation of the circuit in different modes. One notable feature of this power amplifier is its ability to work without any distortion, especially when both frequencies are used simultaneously. Achieving this has been made possible by combining a method that ensures signal termination with a strategy that enables their separation. To evaluate its performance, we conducted computer simulations as tests using both large signals.

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Optimal Location and Size of Solar photovoltaic Generator to Improve the Stability of Iraqi National Super Grid Power System

The Iraqi National Super Grid Power System is facing significant challenges in terms of stability and reliability, leading to power outages and disruptions. One potential solution to this problem is the integration of solar photovoltaic generator (SPVG) into the grid system. This article explores the optimal location and size of solar PV generators in order to improve the stability and reliability of the Iraqi National Super Grid Power System (INSGPS).

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A Highly Compatible Optical/Acoustic Modem based on MIMO-OFDM for Underwater Wireless Communication using FPGA

This work is on Underwater Wireless Communication using optical/acoustic modem based on MIMO–OFDM method. Underwater Communication is a vast field where data analysis is performed on sea exploration, aquatic animals, aquatic species etc. But the current system is based on sound as medium for communication. This system faces many significant problems which plays a key role in affecting the performance of the entire system lost the data and resulting in efficiency of the system only for a few meters radius of transmission and reception. In this paper, the modem designed for both optical (light) and acoustic (ultrasound) signals using MATLAB Simulink and implemented on Xilinx System generator using FPGA.

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Innovative Approach of Spectral Efficiency Optimization over Various Pilot Reuse Factors

The Massive MIMO with TDD is breakthrough technology for spectral efficiency gains. The CSI is essential for spectral efficiency gains and CSI can be obtained by channel estimation methods. The channel estimation methods employ known pilot s sequences to estimate the channel before actual data transmission. However, the channel coherence is time and frequency limited, which reflects the trade-off between the resources available for pilots and those available for data in coherent block for transmission. The pilot sequences reuse in other cells can reduce pilot overhead, called pilot reuse. However potential interference is introduced, by pilot reuse, in the channel estimation phase, called pilot contamination.

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Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods

This paper introduces a pioneering Hybrid Parallel Multi-linear Face Recognition algorithm that capitalizes on multi-linear methodologies, such as Multi-linear Principal Component Analysis (MPCA), Linear Discriminant Analysis (LDA), and Histogram of Oriented Gradients (HOG), to attain exceptional recognition performance. The Hybrid Feature Selection (HFS) algorithm is meticulously crafted to augment the classification performance on the CK+ and FERET datasets by amalgamating the strengths of feature extraction techniques and feature selection methods.

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Performance Analysis of Sensorless Induction Motor Drive using Improved Control Techniques

AC Drives demand robust motor design with rugged construction, low cost, high reliability in service, and simple maintenance. In modern power drives, Sensorless Induction motor drives are more popular than other drives. A speed sensor/encoder-based drive is costlier and requires more space in case more parallel units are coupled together in drive operation. To address these difficulties, speed sensorless drives are introduced without loss of efficiency and reliability. However, Sensorless speed drive requires advanced control techniques in which complex calculations are there due to the nonlinearity of IM.

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An Enhanced Authenticated Key Agreement Scheme for Cloud-Based IoT in Wireless Sensor Networks

Recent advancements in mobile and wireless technology have fundamentally impacted the underpinnings of cloud computing and IoEs. These changes have changed the way data is communicated across numerous channels, allowing for intelligent discovery and operation. The Internet of Things (IoT) is highly reliant on wireless sensor networks (WSNs), which have several applications in industries ranging from smart medicine to military operations to farming. The IoT's substantial reliance on these activities generates a large amount of data. All the above-specified data is transferred to a remote server for storage and processing. As a result, it is critical to enable safe data access in WSNs by authenticating individuals in altered states of awareness.

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Brain Tumor Classification and Identification using PSO and ANFIs

Fast Computer-Aided Diagnostic Systems (CAD) have become instrumental in diagnosing diseases. Brain tumors, in particular, pose a significant health challenge. Traditional tumor detection methods relied on radiologists and biopsy, which are time-consuming and detrimental to patients. Early detection is crucial for effective treatment. This system leverages image processing, SWARM intelligence, and Support Vector Machines (SVMs) to detect and classify brain tumors swiftly and accurately.

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Application of Delta PLC on Battery Management System in AC/DC Microgrid

To keep the energy balance and power quality in check, a proper controller must be made for all the parts of the microgrid to work together. The sizing of the microgrid is done by considering the distributed generators and their connected ACDC loads. The performance of DC will obtain according to the state of charge condition of the battery bank. So it is essential to operate the battery bank by observing the state of the charge condition.

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Performance Analysis of Multi-Hop Hybrid FSO/mm Wave Communication System for Next-Generation Wireless Networks

Next-generation wireless networks are facing increasing demand for high data rates, low latency, and seamless connectivity. To address these challenges, a multi-hop hybrid communication system integrating Free Space Optics (FSO) and millimeter wave (mm Wave) technologies for backhaul communication is proposed. This system combines the advantages of FSO, such as high bandwidth and low latency, with the robustness and reliability of mm Wave technology. The multi-hop architecture enables the formation of a network of interconnected nodes, providing improved coverage and flexibility.

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High-frequency Differential Mode Modeling of Universal Motor's Windings

The universal motor is a rotating electrical machine that can operate on either direct current or single-phase alternating current, similar to a DC motor. It has been widely used in various small and inexpensive drives for a long time, mostly in home appliances and hand tools. The noise generated by a universal motor is believed to be closely associated with the electromagnetic torque fluctuations of the machine, which are caused by variations in the current supplied to the motor. The power electronics utilized for controlling the motor's speed are responsible for these current changes. Accurate high-frequency motor models are crucial for reliable electromagnetic interference simulations in motor drive power electronic systems.

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Optimal Location of Electric Vehicle Charging Station in Reconfigured Radial Distribution Network

Electric vehicles are becoming increasingly popular because they are cleaner-burning and more efficient than combustion-engine automobiles. Due to the diminishing availability of fossil fuels and the carbon emissions produced by cars, electric vehicles (EV) have become a need for mobility in the near future. Electric car charging stations were established as a consequence of the increase in EVs. Electric car charging lowers voltage and increases real power loss in the radial distribution network. In order to mitigate real power loss and provide a stable voltage profile, the charging station has to be placed as efficiently as possible.

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Skin Cancer Detection and Classification using Deep learning methods

Skin cancer is a very dangerous disease that needs to be found early, so that it can be treated effectively. In the past few years, classifiers built on convolutional neural networks (CNNs) have become the best way to find melanoma. According to the review, the CNN-based classifier is as accurate as dermatologist in classifying skin cancer images, allowing for faster and more accurate detection. This article examines the most recent studies on Machine learning and deep learning-based melanoma categorization in depth. We provide a comprehensive description of the machine learning and deep learning classifier, including details on the accuracy of these classifiers.

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Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

Due to its general use in various practical applications, number of innovations in Wireless Sensor Networks (WSN) is receiving a lot of consideration from researchers. It shows significant technological development with excessive capacity since it gives useful information to users in a particular field through real-time monitoring. Due to its characteristics, such as infrastructure-less adoption and resource limitations, wireless sensor networks bring several problems that could impair the system's operation. Cluster based routing in WSN is the major concern in this field that could conflicts with the effectiveness of energy, suitable Cluster Head (CH) selection, protected data transport as well as network lifetime augmentation, demand major consideration, etc.

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A New Dragonfly Optimized Fuzzy Logic-based MPPT Technique for Photovoltaic Systems

Photovoltaic (PV) power systems should be operated at the maximum power point (MPP) for best solar energy utilization, which can be achieved using maximum power point tracking (MPPT) techniques. Perturb & Observe (P&O) and Fuzzy logic MPPT approaches were two of the various strategies that were suggested as effective ways to achieve Maximum Power under Continuous Irradiation. When exposed to changes in environmental conditions, these approaches perform poorly dynamically and exhibit substantial steady-state oscillations around the MPP. To overcome this problem, this paper proposes the Dragonfly optimization-based fuzzy logic MPPT approach for maximum power extraction of photovoltaic (PV) systems.

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A Novel Optimized Neural Network Model for Ink Selection in Printed Electronics

The field of Printed Electronics (PE) is experiencing significant growth in the industrial sector and generating considerable interest across various industries due to its ability to produce intricate components. The functionality of printed electronic products heavily relies on the utilization of conductive ink during the printing process, which plays a vital role in developing flexible electronic circuits and improving the communicative functionalities of objects. Selecting the right ink for printing is crucial to meet consumer requirements. However, the conventional approach to this process has been manual, labor-intensive, and time-consuming, relying on the expertise of designers.

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Optimized Multi Agent System for Stability Enhancement of Inter Connected Power System

Due to the rising use of renewable energy sources and the use of contemporary power electronic equipment, power system stability has become a major challenge in current power systems. Controlling the power system characteristics can increase the stability of the power system. The traditional techniques for improving power system stability, such as the use of FACTS devices, are costly and may not be effective in handling the dynamic changes of the power system. As a result, by optimizing the power system parameters, an optimization-based multi-agent system can improve the stability of the power system. The Grey Wolf Optimizer based Multi Agent System (GWO-MAS) is proposed in this paper to improve power system stability.

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Control of Three-Phase Squirrel Cage Induction Motor by Field Acceleration Method (FAM) for E-Mobility

The rapid electrification of mobility systems has fuelled the demand for advanced control techniques that can enhance the performance and efficiency of electric vehicles (EVs). In this context, this paper introduces the Field Acceleration Method (FAM) as a control strategy for three-phase squirrel cage induction motors, specifically tailored for e-mobility applications. FAM has not been previously simulated or tested practically in the context of electric mobility, making this study a pioneering effort. Induction motors are widely employed in electric vehicles, and various control methods such as the Indirect Field-Oriented Control (IFOC) gained popularity for its effectiveness in achieving precise and efficient motor control.

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MRI Intracranial Neoplasm Localization Using Convolution Neural Network Based Residual Block ResUnet: A Systematic Approach

Analysis of intracranial neoplasm using multimodal MR images requires accurate and automatic segmentation. However, manually classifying tumors with similar structures or appearances in magnetic resonance imaging (MRI) with similar anatomy or appearances is more challenging, requiring experience to detect brain tumors. Precise segmentation of brain tumors gives clinicians with a foundation for surgical planning and treatment. Due to its capacity to segment brain tumor images automatically, Deep Neural Networks (DNN) have been widely used in image segmentation applications.

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A Fuzzy Logic Based Cluster Head Election Technique for Energy Consumption Reduction in Wireless Sensor Networks

Wireless sensor networks deploy sensor nodes to different areas for data collection. The small size of these sensor nodes allows limited energy storage capacity, and most applications of the networks do not support recharging the batteries once their energy is depleted. Research on energy efficiency in wireless sensor networks is thus an active area that seeks to minimize energy consumption so that the sensor nodes can live longer. Clustering, one of the energy consumption optimization techniques, is employed in this research. It splits the network into smaller groups for data collection and forwards the data to the base station via appointed cluster heads.

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Reconfigurable Converter Topologies for EV Fast Charging Stations

Infrastructure for charging electric vehicles (EVs) is highly demanded due to the rising number of EVs on the road. Stations for charging electric vehicles are necessary for the ongoing transportation e-mobility. In particular, fast charging infrastructures increase the computing ability of transmission grids that are already under a lot of pressure. The market's current energizing foundation takes a ton of room and incidentally causes gridlocks, which raises the risk of mishaps and hinders crisis vehicles. The cost of installing this charging infrastructure increases significantly because the current system needs a lot of room.

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GWOPID-MRAS based Speed Estimation and Speed Control of Sensorless Induction Motors

Sensorless AC motor drives have grown in popularity recently in a variety of applications, from industrial to domestic electrical equipment. FOC and DTC are popular control methodologies in contemporary alternating current (AC) structures. They can achieve good performance for AC motor drives by creating a decupled flux and torque control. However, both have limitations and drawbacks, such as the vector control's dependence on machine parameters and DTC's high flux and torque ripples. This paper proposes a new control method, in which the speed of an induction motor is controlled by Grey Wolf Optimizer (GWO) based PID controller.

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Modeling, Design and Control of Speed DC motor using chopper

An electric motor, a power controller, and an energy-transmitting shaft make up an electrical drive. Power electronics converters are utilized as power controllers in contemporary electrically driven systems. DC actuators and AC drives are the two primary categories of electric drives. This study presents design and modeling techniques for very effective individually stimulated DC motor speed control. A DC motor speed controller can be implemented using a chopper circuit as a converter. The controller transmits a signal into the chopper firing circuit, which in turn generates the desired speed of the chopper by varying the voltage supplied to the motor's armature.

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Design of Boost Integrated Luo Converter for Grid Tied EV Based Charging Station

The combination of Renewable Energy Source (RES) and storage element in charging station is a possible solution for meeting the growing energy requirement of electric vehicles (EVs). In a grid-tied RE system, the converters are essential for attaining the process of power conditioning. Conventional DC-DC converters suffer from issues like voltage spikes, electromagnetic interference, and efficiency losses. The necessity for integrated converters arises from the demand for improved power conditioning, increased power density, and enhanced overall system performance.

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Proficient Bayesian Classifier for Predicting Congestion and Active Node Sensing Classification in Wireless Cognitive Radio

This study researches into fixed range designation systems with diverse applications in remote sensing, specifically addressing the emerging issue of range deficiency, particularly concerning access points with reduced range delivery services for remote hubs. An analysis of the existing system reveals limitations in current approaches. To overcome these challenges, the study proposes leveraging remote cognitive radio, a dynamic range access approach that optimally utilizes existing resources. The central focus of cognitive radio is on acquiring sensing data, addressing the deficiencies observed in the existing system.

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Development of Static Model for a Current Based UPFC

The power system loads are dispersed across the network and generators are concentrated in a few key locations. In between generation and loads, there exist transmission systems. FACTS devices have applications to regulate voltage magnitude and angle, and impedance of the system. In FACTS devices, UPFC is one of its kind. Generally, line outages are occurred due to the faults on the transmission lines. One of the sensitive measures to understand the line outages is Line Outage Distribution Factor (LODF).

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Multiple Grid-Connected Microgrids with Distributed Generators Energy Sources Voltage Control in Radial Distribution Network Using ANFIS to Enhance Energy Management

Voltage conditions and power quality for customers and utility equipment are significantly impacted by the addition of microgrid-generating sources within distribution networks. Designing the right control for distributed generators for the various generating units of a Microgrid is important in enabling the synchronization of renewable energy generation sources, energy storage unity, and integration of Microgrids into a radial distribution network. This research provides control mechanisms based on an adaptive technique employing ANFIS, to reduce fluctuation of voltage and current difficulties faced when multiple renewable energy sources and storage systems are incorporated into a distribution network.

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L-M Based ANN for Predicting the Location of DG under Contingency Condition

The continuing monitoring of online voltage stability and the increased loadability of the transmission lines for the existing electrical power system are the two major challenges that today's energy management systems must deal with. As a result, evaluating online voltage stability under various loading situations is extremely challenging and time-consuming. The line voltage stability indices using an Artificial Neural Network (ANN), the system describes online voltage monitoring and warns the operator before voltage dips.

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Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments

This study leverages the Semantic Segmentation of Underwater Imagery (SUIM) dataset, encompassing over 1,500 meticulously annotated images that delineate eight distinct object categories. These categories encompass a diverse array, ranging from vertebrate fish and invertebrate reefs to aquatic vegetation, wreckage, human divers, robots, and the seafloor. The use of this dataset involves a methodical synthesis of data through extensive oceanic expeditions and collaborative experiments, featuring both human participants and robots. The research extends its scope to evaluating cutting-edge semantic segmentation techniques, employing established metrics to gauge their performance comprehensively.

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Development of Wireless Smart Current Sensor for Power Monitoring System

The traditional distribution network must be replaced by a smart grid, durable, and dependable, due to the rising demand for power by the customer. The smart grid will need a smart monitoring system based on the smart current sensor at all buildings to determine the power consumption with its costs. This paper proposed a wireless monitoring system for measuring the three-phase currents in the building by using three HW-666 current sensors with Arduino and ESP32 microcontrollers to transmit the data according to the Internet of Things (IoT) technique to the Telegram app on the mobile phone.

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Vertebra Segmentation Based Vertebral Compression Fracture Determination from Reconstructed Spine X-Ray Images

The vertebral compression fracture represents the vertebral body deformity appeared over lateral spine imageries. In order to evaluate the vertebral compression fracture (VCF), the vertebral compression ratio (VCR) has to be accurately measured. In most of the existing vertebral segmentation approaches, degraded accuracy, increased possibilities of error and time complexity are found to be the major drawbacks. Hence to conquer these issues and to enhance the overall segmentation performance, rapid automated vertebral segmentation approach is proposed for evaluating the VCR.

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Frequency Stability of Multi-source Power System using Whale Optimization Algorithm

The Whale Optimization Algorithm (WOA), an evolutionary computing approach, is presented in this study and is used to auto regulated frequency of many composed power systems including thermoelectric power station, hydroelectric, and gas power plants. The purpose of this process follows the concept of a hunting mechanism of fish through water bubbles. The WOA is first applied to a single region with a multi-source power system for optimal gain adjustment of proportional integral controllers (PID). This approach is then applied to two areas, each having six generating sources with AC and AC-DC links.

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Multiple Power Quality Issues Reorganization Analysis and Feature Extraction with the Discrete Wavelet Transform

Now a day’s utilization of the power is very important concept in term of the quality. Utilization of the power is very effective as compared to the generation, at the end point of the different issues are occurs when the power is uses these issues are affect the quality of power so in this paper present about the application of the wavelet for determination of the different power quality issues in the system. In this series first issues determination play very important role.

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Development of Smart Agriculture to Detect the Arabica Coffee Leaf Disease using IAFSA based MSAB with Channel and Spatial Attention Network

Plant diseases provide challenges for the agriculture sector, notably to produce Arabica coffee. Recognising issues on Arabica coffee leaves is a first step in avoiding and curing illnesses to prevent crop loss. With the extraordinary advancements achieved in convolutional neural networks (CNN) in recent years, Arabica coffee leaf damage can now be identified without the aid of a specialist. However, the local characteristics that convolutional layers in CNNs record are typically redundant and unable to make efficient use of global data to support the prediction process.

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Dynamic Monitoring and Analysis of Dual-Axis Movement of SPV Power Plant Parameters under Various Atmospheric Conditions

This research paper presents comprehensive insight into the testing of a 1 KW sun tracking photovoltaic (PV) performance. The study uses a state-of-the-art real-time string monitoring system to allow this analysis while covering an extensive variety of atmospheric conditions. The design of the 1 KW solar tracker system incorporates a tracking sensor circuit, motor driver circuit, string monitoring system, and solar tracker control circuit. Solar tracking systems boost energy generation by adjusting the angle of PV panels to optimum sunlight exposure.

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